The DSGE method provides a microeconomic foundation for the study of the impact of exchange rate changes and monetary policy shocks on domestic investment and exports (Scholl & Uhlig, 2008). The model’s specification considers rational expectations and inter-temporal optimisation, as well as market clearing conditions. However, it also allows for a range of nominal and real rigidities (Eichenbaum et al., 2020). The DSGE models represent various economic agents acting in a small open economy. Households maximise their utility within a budget constraint, firms maximize their profits subject to technology and adjustment costs, the central bank conducts monetary policy, the government conducts fiscal policy, and the foreign sector influences external conditions. The representative household’s objective is to maximize expected lifetime utility, given the discount factor:

$E_0\sum _{t=0}^{\infty }{\beta }^{t}U(C_t,H_t,\frac{M_t}{P_t})$ (1)

Note that C_t is consumption, H_t is hours of labour worked, M_t /P_t is real money held, and t is the discount factor. The utility function is specified as:

Household Sector: The utility relation of the household is as given in equation (2).

$U(C_t,H_t,\frac{M_t}{P_t})=\frac{C_t^{1-{\sigma }_C}}{1-{\sigma }_C}-\frac{H_t^{1+{\sigma }_H}}{1+{\sigma }_H}+\chi \frac{{\left(M_t/P_t\right)}^{1-{\sigma }_M}}{1-{\sigma }_M}$ (2)

The household’s budget constraint is:

P_tC_t + M_t + B_t + E_tB_t^* = W_tH_t + M_t–1 + R_t–1Bt–1 + E_tR_t^*B^*_t–1 + Π_t + T_t (3)

where B_t and B_t^* are domestic and foreign bonds, E_t is nominal exchange rate, W_t is the nominal wage, R_t and R_t^* are domestic and foreign interest rates, Π_t represents profits from firms, and T_t are transfers.

Domestic Firms: Domestic firms operate in monopolistically competitive markets and face price adjustment costs. The productions function for firm j is as specified in equation (4):

Y_t(j) = A_tH_t(j)^αK_t–1(j)^1–α (4)

where A_t is the effect of technology, H_t(j) is labor man-hour input, and K_t(j) is capital stock. Capital accumulation follows:

$K_{t+1}\left(j\right)=(1-\delta )K_t\left(j\right)+I_t\left(j\right)|1-\frac{{\varphi }_t}{2}{(\frac{I_t\left(j\right)}{I_{t-1}\left(j\right)}-1)}^2|$ (5)

where I_t(j) is investment, δ is depreciation rate, and ϕ_I represents investment adjustment costs.

Price Setting: Firms’ price change or adjustment costs equation is given in (6):

$\complement \left(\pi \right)=\frac{{\vartheta }_p}{2}{({\pi }_s-1)}^2{Y}_f$ (6)

where ϑ_p account for price stickiness (price adjustment cost) parameter and π_s is steady-state inflation or gross inflation rate that defines the change in current prices by firms relative to previous prices and Y is national output.

Export Sector: The export demand follows a detailed specification that incorporates the key variables of the study:

DEXP_t = f [P_EXP,t,S_t,FINDEV_t,POLUNC_t,P_t^*,Y_t^*,η_EXP,η_FIN,η_Y,η_POL,ξ_EXP,t} (7)

where DEXP_t is total exports, P_EXP,t is the domestic export price, S_t is the nominal exchange rate, P_t^* is the foreign price level, Y_t^* is foreign output/demand, FINDEV_t is the financial development index, POLUNC_t is the policy uncertainty index, η_EXP is the price elasticity of export demand, η_Y is the income elasticity of export demand, η_FIN captures financial development effects on export capacity, η_POL captures policy uncertainty effects on export performance, ξ_EXP,t is an export-specific shock. The export price is determined by:

$P_{(EXP,t)}={\mu }_{E}XPM{C}_t(1+{\tau }_{EXP,t}){\left(\frac{FINDE{V}_t}{FINDEV}\right)}^{-\gamma FIN}(1+{\gamma }_{POL}POLUN{C}_t)$ (8)

where μ_EXP is the export markup, MC_t is marginal cost, τ_EXP,t represents export-related costs including financing costs affected by monetary policy, γ_FIN captures how financial development reduces export costs, and γ_POL captures how policy uncertainty increases export costs. The export firms face working capital requirements that depend on monetary policy:

$W{C}_{\mathrm{EXP},t}={\theta }_{\mathrm{EXP}}{P}_{\mathrm{EXP},t}EX{P}_t{\left(\frac{R_t}{R}\right)}^{{\theta }_{\mathrm{WC}}}$ (9)

where θ_EXP is the working capital coefficient and ϕ_WC captures the sensitivity to interest rates.

Monetary policy specification: The central bank follows an enhanced Taylor-type rule that incorporates money supply considerations and responds to both domestic and external conditions interest rate smoothing. The enhanced Taylor-Type rule is as specified:

$\begin{array}{c}RtR=(Rt-1R)\rho R\left[\left(\pi t{\pi }^{\ast }\right)\varphi \pi \right(YtY\left)\varphi Y(\frac{St}{St}-1{\pi }^{\ast })\varphi S\right]1-\rho \mathrm{Re}[R,t\frac{R_t}{R}=\\ ={\left(\frac{R_{t-1}}{R}\right)}^{p_R}{\left[{\left(\frac{{\pi }_t}{{\pi }^{\ast }}\right)}^{{\varphi }_R}{\left(\frac{Y_t}{Y}\right)}^{{\varphi }_Y}{\left(\frac{S_t/S_{t-1}}{{\pi }^{\ast }}\right)}^{{\varphi }_s}\right]}^{1-{\rho }_R}{e}^{J_{Rt}}RRt=\\ =(RRt-1)\rho R\left[\left({\pi }^{\ast }\pi t\right)\varphi \pi \right(YYt\left)\varphi Y\left({\pi }^{\ast }St/St1\right)\varphi S\right]1-\rho \mathrm{Re}\int R,t\end{array}$ (10)

Money supply follows an enhanced specification:

$\frac{M_t}{M}={\left(\frac{M_{t-1}}{M}\right)}^{{\rho }_M}{\left(\frac{R_t}{R}\right)}^{-{\varphi }_{MR}}{\left(\frac{Y_t}{Y}\right)}^{{\varphi }_{MY}}{e}^{{ϵ}_{M,t}}$ (11)

where ρ_R and ρ_M are persistence parameters, ϕ_π, ϕ_γ, ϕ_S, ϕ_MR, and ϕ_MY are policy response coefficients, and ${ϵ}_{R,T}$ and ${ϵ}_{M,t}$ are monetary policy shocks.

Credit supply is determined by the specification of equation (12):

${\text{CREDIT}}_t=\overline{CREDIT}{\left(\frac{FINDE{V}_t}{FINDEV}\right)}^{{\gamma }_{\text{CRED}}}{\left(\frac{R_f}{R}\right)}^{-{\varphi }_{\text{CRED}}}{e}^{e_{\text{CRED},t}}$ (12)

where γ_CRED captures financial development effects on credit supply and ϕ_CRED captures interest rate effects.

Fiscal policy: With regard to government expenditure, ĝ_t is fraction of the deviation of government spending from its steady state, γ_g is persistence parameter that measure how long the fiscal programs would last or persists and is a fiscal policy shock. The relationship is represented in equation (13).

${\hat{g}}_1={\gamma }_s{\hat{g}}_{t-1}+{\epsilon }_t^8$ (13)

Foreign Sector and Exchange Rate: The exchange rate is based on interest parity that incorporates risk premiums, financial development, and policy uncertainty. The foreign sector equations are given as:

$E_t\left[\frac{S_{t+1}}{S_t}\right]=\frac{R_t^{\ast }}{R_t}{e}^{{\psi }_{RPt}}$ (14)

where the risk premium follows an enhanced specification:

$\begin{array}{cc}{\psi }_{RP,t}& ={\psi }_0+{\psi }_1{\left(\frac{FINDE{V}_t}{FINDEV}\right)}^{-{\psi }_2}+{\psi }_{3}POLUN{C}_t+\\ & +{\psi }_4\left(\frac{CREDI{T}_t}{CREDIT}\right)+{\psi }_5\left(\frac{B_t^{\prime }GD{P}_t}{b^{\prime }gdp}\right)+{ϵ}_{RP,t}\end{array}$ (15)

This specification shows that financial development reduces the risk premium (higher FINDEV lowers the risk premium through ψ₁ > 0, ψ₂ > 0), policy uncertainty increases the risk premium (through ψ₃ > 0; credit conditions affect the risk premium (through ψ₄); and external debt levels affect the risk premium (through ψ₅ > 0). The model includes several stochastic processes that drive the key variables of the study:

Technology shock (in deviation from steady state):

${\hat{a}}_t={\gamma }_a{\hat{a}}_{t-1}+e_{a,t}$ (16)

Export demand shock:

${\xi }_{EXP,t}={\rho }_{EXP}{\xi }_{EXP,t-1}+{\sigma }_{EXP}{ϵ}_{EXP,t}$ (17)

Policy uncertainty shock:

$\text{POLUNC}{}_t=(1-{\rho }_{POL})\overline{POLUNC}+{\rho }_{POL}POLUN{C}_{t-1}+{\sigma }_{POL}{ϵ}_{POL,t}$ (18)

Financial development evolution:

$\log FINDE{V}_t=(1-{\rho }_{FIN})\log \overline{FINDEV}+{\rho }_{FIN}\log FINDE{V}_{t-1}+{\sigma }_{FIN}{ϵ}_{FIN,t}$ (19)

Foreign demand shock:

$y_t^{\ast }={\rho }_{y^{\prime }}\cdot y_{t-1}^{\ast }+e_{y^{\prime },t}$ (20А)

Risk premium shock:

${ϵ}_{RP,t}={\rho }_{RP}{ϵ}_{RP,t-1}+{\sigma }_{RP}{v}_{RP,t}$ (20)

Monetary policy rate shock:

${ϵ}_{R,t}={\rho }_{R,\text{slock}}{ϵ}_{R,t-1}+{\sigma }_R{v}_{R,t}$ (21)

Money supply shock:

${ϵ}_{M,t}={\rho }_{M,\text{slock}}{ϵ}_{M,t-1}+{\sigma }_M{v}_{M,t}$ (22)

Credit shock:

${ϵ}_{\text{CRED},t}={\rho }_{\text{CRED}}{ϵ}_{\text{CRED},t-1}+{\sigma }_{\text{CRED}}{v}_{\text{CRED},t}$ (23)

where all ϵ and ν terms represent white noise innovations with zero mean and unit variance, â_t is percentage deviation of technology from its steady state, γ_a is persistence parameter that measure how long the technology upgrading persists and e_a,t is shock innovation. A logarithmic linearization of in its steady state yields a system of linear rational expectations equations that can be solved using standard techniques. The key equations for the study variables are: The log-linearized investment, exports, exchange rates, monetary policy rule, money supply and credit equations are given by equations (24), (25), (26), (27), (28), and (29) respectively.

$\begin{array}{cc}\hat{I}t=& S_t\hat{I}t+1-{\sigma }_l(\hat{R}t-S_t\hat{\pi }t+1)+{\gamma }_E\hat{S}t+{\gamma }_{c}C\stackrel{⏜}{REDIT}t+\\ & +{\gamma }_F\stackrel{⏜}{FINDEV}t-{\gamma }_P\stackrel{⏜}{POLUNC}t+{\gamma }_{MS}\stackrel{⏜}{MS}t+ϵI,t\end{array}$ (24)

$\begin{array}{c}\widehat{EXPt}=-{\eta }_p({\hat{P}}_{EXP,t}-{\hat{S}}_t-\hat{P}{t}^{\prime })+{\eta }_Y{\hat{Y}}_t^{+}{\eta }_{FIN}\widehat{FINDEV}t-\\ -{\eta }_{POL}\widehat{POLUNC}t-{\gamma }_{E}ER({\hat{R}}_t-\hat{\pi }t)+{\gamma }_{MS}\widehat{MSt}+{\gamma }_C\widehat{CREDIT}t+\hat{\xi }{\widehat{EXP}}_t\end{array}$ (25)

$\begin{array}{cc}\hat{S}t=E_t\hat{S}t& +1-({\hat{R}}_t-{\hat{R}}_t^{\ast })+{\psi }_1+{\psi }_1\widehat{MSt}+{\psi }_2{\widehat{\text{FINDEV}}}_t+\\ & +{\psi }_3{\widehat{\text{POLUNC}}}_t+{\psi }_4\widehat{\text{CREDIT}}t+\widehat{ϵ}RP\end{array}$ (26)

$\hat{R}t={\rho }_R\hat{R}t-1+(1-{\rho }_R)[{\varphi }_{\pi }{\hat{\pi }}_t+{\varphi }_Y\hat{Y}t+{\varphi }_S(\hat{S}t-\hat{S}t-1\left)\right]+\widehat{ϵ}R,t$ (27)

$\widehat{MS}t={\rho }_M\widehat{MS}t-1-{\varphi }_{MR}\hat{R}t+\varphi MY\hat{Y}t+\widehat{ϵ}M,t$ (28)

$\widehat{CREDIT}t=\gamma CRED\widehat{FINDEV}t-\varphi CRED\hat{R}t+\widehat{ϵ}CRED,t$ (29)

where: ${\hat{I}}_t,{\widehat{EXP}}_t$, etc. denote log-deviations from steady state; ${\sigma }_I$ is the intertemporal elasticity of substitution for investment; γ_E,γ_C, γ_F, γ_p, γ_MS are coefficients capturing exchange; rate, credit, financial development, policy uncertainty, and money supply effects on investment; ${\eta }_P$, ${\eta }_Y$, ${\eta }_{FIN}$, ${\eta }_{POL}$ are elasticities of export demand with respect to prices, foreign income, financial development, and policy uncertainty; ${\gamma }_{ER},{\gamma }_{EMS}$, ${\gamma }_{EC}$ are coefficients capturing real interest rate, money supply, and credit effects on exports. The equations demonstrate the manner in which exports are affected by interest rates, exchange rates and other macroeconomic variables, thereby establishing the foundation for the examination of policy transmission mechanisms. In some countries such as Nepal, Tajikistan and the Philippines remittances tend to compete or even surpass export earnings (Azam et al., 2016; Qurbonova & Zaidi, 2012). Remittances serve as an important source of foreign exchange and an important shock absorber to households in those countries (Yang & Choi, 2007). Therefore we expand the DSGE model to include the informal sector, barter (non-monetized transactions) as follows:

The results of the prior distributions reported in Table 3 below are highly sensitive. Using standard priors based on developed economies (e.g. the United States or the Eurozone) would most probably result in model misspecification and incorrect inference when applied to the 16 emerging and developing Asian markets, due to their fundamental structural differences. Consequently, the Bayesian prior distributions fundamentally restructure traditional macroeconomic assumptions to accurately reflect the subsistence-driven realities of the poorest Asian economies. By significantly increasing the inverse inter-temporal elasticity parameter, the model mathematically captures the acute hand-to-mouth behavior of populations that lack formal savings vehicles and cannot easily smooth their consumption over time. This structural vulnerability is further reinforced by a downward adjustment in the habit formation prior, acknowledging that subsistence households must consume based on immediate daily availability rather than maintaining stable, persistent consumption patterns. The formal sector weight and the monetization ratio formally quantify the massive scale of the shadow economy, revealing that nearly half of national output and a vast portion of trade bypass fiat currency entirely. As these informal and barter markets operate with daily unregulated transactions, the aggregate Calvo price stickiness parameter is notably lowered to blend rigid formal pricing with extreme informal price flexibility. Despite this pervasive underlying volatility, the priors for the Taylor rule parameters remain rigidly anchored, reflecting the reality that central banks in these regions continue to aggressively target formal-sector inflation while treating long-term output growth as a secondary mandate (Klau & Mohanty, 2004; Ybrayev, 2021). To account for the vital external financial lifelines sustaining these households, the model assigns a remarkably high persistence prior to the newly introduced remittance parameter. This elevated persistence mathematically dictates that worker transfers act as a deeply ingrained, structural shock absorber that continuously influences the real effective exchange rate independently of domestic monetary policy interventions.

Concurrently, the persistence and magnitude of broader external macroeconomic shocks remain firmly elevated within the inverse gamma and beta distributions, a necessary configuration to accommodate the inherent vulnerability of frontier markets to sudden global commodity shifts. This all-inclusive recalibration ensures that the subsequent empirical estimations do not artificially force advanced-economy dynamics onto deeply fragmented, cash-reliant and remittance-dependent nations. The open-economy, small-economy argument suggests that the Asian economies are vulnerable to external shocks due to their integration into the international financial cycle, given the increased macroeconomic volatility it implies (Deb et al., 2021). Financial markets in developing Asian economies are shallower and more fragmented than those in the West (Didier & Schmukler, 2014). Household balance sheets also exhibit distinct patterns compared to those in advanced economies (Badarinza et al., 2019). As a result, we use smaller prior means of the parameter of habit-formation (h) and larger prior standard deviations of the inverse of the inter-temporal elasticity of substitution (σ_c) in opposition to the benchmark of Smets and Wouters (2007). In addition, the central banks of Asian countries do not typically adhere to a pure Taylor rule based on inflation and the output gap; rather, they employ aggressive managed-float exchange-rate regimes (Kubo, 2022). In light of this, we introduce a non-zero exchange rate coefficient into the monetary policy rule to reflect widespread panic surrounding floating, and we revise the parameters of consumption and investment friction to simulate the liquidity constraints that Asian households and firms face. This strategic divergence from standard priors creates diffusive priors for shock processes, incorporating the increased volatility and abrupt stop risks typical of the Asian region.

The analysis of identification, which is summarized in Table 4, confirms that the key parameters of the DSGE model, including: the monetary policy reaction coefficient (ϕ₁) and the exchange-rate sensitivity (α₁), are identified because of the direct observational relations to the data of inflation and exchange-rates. These parameters are well supported by the empirical estimates, and this is a positive indicator of their estimation. Conversely, the risk aversion coefficient (γ) and output shock coefficient (σ₃) are less well-defined, due to the latency of risk preferences and the confounding effect of other macroeconomic variables. Precisely estimated parameters of inflation targeting (π₁) and export demand sensitivity (η₁) provide a robust empirical basis for informing policy decisions. The model ensures that the best estimates of the various parameters are uniquely determined by the data and economic theory, thus proving the accuracy of the findings obtained.

Figure 1 shows how export growth responds to different monetary and fiscal scenarios. The model was calibrated using Asian data from 2000 to 2025, with a simulation horizon of 20 quarters. During the coordinated expansion, the peak impact was observed in the fifth quarter at 78%, indicating a very strong positive export response to the coordinated monetary and fiscal stimulus. Conversely, the trough impact is -30%, indicating negative export effects resulting from contractionary monetary policy.

As illustrated in Figure 2, the coordinated policy expansion versus policy conflict scenarios comparison displays +78% growth in exports by the fifth quarter when monetary and fiscal policies are synchronized. The negative reaction of -0.112% arriving at quarter 8 shows how inconsistent policies can erode the trust of investors. The main takeaway is that the regulating policies demonstrate immediate positive effects with early peak impact whereas the policy conflicts result in the delayed negative responses accompanied by the persistent volatility in the investors’ behavior.

As shown in Figure 3, the effect of exchange rate depreciation was most beneficial in the fifth quarter, with a positive value of +0.656%. This indicates that currency depreciation has a long-lasting competitive effect on trade. Uncertainty over policies had the least influence on exports, at -0.378%, at the end of the fourth quarter. This suggests that the decline in business confidence is negatively impacting the export sector due to policy uncertainty. The broad trade policy insight is that the 10% currency devaluation (exchange rate depreciation) is a key factor to be relied upon for export growth that is both significant and long-lasting, while the policy uncertainty brings in negative impacts that are instantaneous but last for several quarters during which a slow recovery takes place.

Figure 4 illustrates the situation where exchange rate depreciation of 10% caused the peak positive impact of +0.867% at the end of the year, indicating the very strong and quick gain in export competitiveness due to currency depreciation in Asia. The coordinated expansion similarly highlights the peak impact of +0.934% in the fifth quarter, which can be interpreted as the exceptional stimulus effect on export growth coming from the monetary and fiscal measures. Conversely, the monetary contraction scenario involving a 1% increase in the MPR resulted in a trough impact of -0.545% in the sixth quarter, indicating that contractionary monetary policy had a very negative and extensive impact on export performance. The suggested export policy for Asia is that changes in the exchange rate have a significant impact on exports from Asia. For example, a 10% depreciation in the currency can lead to sustained export growth of over 0.8% for several quarters.

On the other hand, contractionary monetary policy brings about huge and lasting adverse effects, while the adoption of expansionary monetary policy can lead to the most powerful positive response in exports. These effects are indeed much more significant than those experienced in Europe, as they directly reflect the higher price elasticity and external dependency typical of Asian developing economies (Senhadji & Montenegro, 1999).

The chart presented in Figure 5 indicates the maximum positive effect of +0.434% in the 5th quarter, accompanied by the continuous effect of 8-10 quarters; this shows that the financial sector’s development is crucial for the export competitiveness. The impact of policy uncertainty was manifested through a negative effect of -0.534% in the 4th quarter, which resulted in immediate and severe disruptions of the export activities with a slow recovery in the 6-8th quarters. Credit Expansion is reported to have the highest effect of +0.467% in the 4th quarter, indicating that access to credit has a direct and major impact on export performance. The Asian Development Economics perspective is that the financial development transition results in substantial and lasting benefits for Asian export performance, with the latter’s sustainability being much longer than that of the former’s impact of monetary policy. Immediate and severe disruptions resulting from policy uncertainty are very hard to recover from, while credit expansion brings about strong positive effects on financial development. Such developments in the financial sector should be considered the primary option, while stability in government policies is needed to support export competitiveness in developing Asian countries.

As illustrated in Figure 6 below, the export sensitivity comparison to the main variables indicates that the REER at +0.867% in Asia was the most responsive to a 10% depreciation, while the financial development effects at +0.434% were the second most responsive to a 1σ improvement. The policy de-ranking effects were at -0.534% trough response to 1σ increase. It can be concluded that Asian economies are the most sensitive to changes in exchange rates with regard to exports. Therefore, it can be inferred that they are more integrated into global value chains, and that their export demand is more elastic. The same can be said of the negative response to policy uncertainty, which was the most sensitive issue and hence more susceptible to institutional instability. At the same time, the response to financial development was robust, confirming the assumption that financial sector development policies have great potential to attract benefits.

The results of the DSGE export response simulations for the Asian countries included in the study are presented in Table 5. The Bayesian DSGE simulations show that policy coordination and exchange rate movements emerge as the main factors influencing Asia’s export growth. Indeed, 10% depreciation of the exchange rate is the most dominant and persistent positive independent shock, with a quick rise to 0.88% in the 4th quarter, with a credible interval above zero. The evidence also highlights the prudence of a massive asymmetry in the effectiveness of monetary policy, with a 1% contraction having a far greater negative impact on exports than a 1% expansion has a positive impact. But, when monetary and fiscal expansions are simultaneously applied, this twin shock has the largest overall magnitude in the model, with a spectacular peak.

During the impact phase, export growth shrinks by -0.45 percent, capturing the initial cost shock for export intermediate imports. Although this cost shock is short-lived, a powerful expenditure-switching effect ultimately catapults the export curve to a massive peak expansion of 1.42 percent in the fourth quarter. Despite the audacity of this growth, the confidence interval of 0.95 per cent to 1.88 per cent indicates significant variation across countries in the effectiveness of firms’ responses to a weak currency. To maintain global competitiveness without the whims of currency manipulation, the quantitative findings heavily favour institutional financial development. A one standard deviation positive shock to financial development yields a cumulative increase in the peak effect of 0.72 percent in the 12th quarter. Notably, it is the only shock which leaves a permanent, positive structural boost of 0.55 percent. Although raw domestic credit boom also delivers a quantitative benefit of 0.38 percent peak in the second quarter, the quantitative effects completely evaporate in the long run. On the downside, the simulation shows the devastating impact of domestic volatility and infrastructural gaps. A one standard deviation shock to economic policy uncertainty immediately erodes manufacturers’ trust, triggering a rapid quantitative collapse of -0.68 percent in the second quarter. Physical bottlenecks mirror this psychological damage as a one standard deviation shock to energy intensity requires a -0.56 percent decline in exports. This metric provides the absolute binding constraints imposed by the weak electric grid on industrial production. The multifaceted nature of these constraints results in the need to employ a combined approach to counter these effects, as is the case in the coordinated expansion scenario. Through the simultaneous implementation of monetary, credit and structural interventions, the coordinated shock generates a very robust peak export growth of 0.88 percent in the fourth quarter. This coordinated approach is theoretically demonstrated to be more effective than monetary fiddling, and yields a persistent 0.10 percent impact in equilibrium. But the full extent of the region’s vulnerabilities is laid bare by defining the new structural frictions. A mere one percent shift of labor towards the informal economy unleashes an aggressive factor misallocation, which depresses formal exports by -0.52 percent at its peak. This coefficient exposes the vicious zero-sum competition, where informal markets systematically drain precious human and physical capital from internationally competitive manufacturing industries. Agriculture records its own rural paralysis, captured via the barter friction shock. A one standard deviation increase in search costs for barter induces -0.22 percent peak decline, amounting to the effective isolation of rural surpluses from global monetization opportunities. The effect of this rural isolation is reinforced by a one standard deviation shock to non-monetized trade preferences, which induces a lagging peak negative export shock of -0.35 percent in the fourth quarter. This particular period proves that when domestic agents positively eschew fiat currency for domestic payments, the formal export sector is systematically denied the domestic liquidity needed to fund supply chains. These point estimates demonstrate that unless policymakers decisively break down the pervasive structures of informality and barter, macroeconomic policies will continue to be unable to support sustainable exports.

It is evident that structural financial factors engender significant externalities. It is demonstrated that both financial deepening and direct credit expansion function as potent stimuli for trade capacity, with a short-to-medium-term effect. This aligns with the finding of Manova (2012) and Paravisini et al. (2011). While isolated monetary shocks can have long-run effects, the structural policy benefits of well-developed financial markets are permanent as earlier found by Bekaert et al. (2010), and Jordà et al. (2020). Conversely, policy uncertainty has been identified as a significant macro-friction, exerting a negative influence on export growth (Kristina, 2026; Fan et al., 2025). The export vulnerability of the region’s manufacturing sector is also nicely measured by the long-term negative impact of an energy intensity shock. This finding aligns with the results reported by Dussaux & Monjon (2023) and Festić (2026). It is imperative that policymakers recognize the inherent practical uncertainty of frontier Asian markets, and in response, implement integrated macro-financial policy commitments as opposed to monetary policy responses. Ultimately, the most effective ways to promote export resilience are to maintain a competitive exchange rate, develop financial markets and stabilise policies. This aligns with Aizenman et al. (2016), Chaney (2016), Aftab & Phylaktis (2022) and O’Donnell et al. (2024).

Table 6 indicates the country-specific disaggregated responses of the DSGE exports. The numerical asymmetries in the absorption of macroeconomic shock by the transitional economies are shown by the disaggregated quantitative information of the dynamic stochastic general equilibrium models. In all the countries studied, 10 percent exchange rate depreciation is the most universally powerful short-term driver of export acceleration. Such depreciation always boosts export volumes to their maximum in the fourth quarter, albeit with considerable country-to-country variation in the numerical elasticity of such depreciation. The fastest fourth-quarter growth is in highly integrated manufacturing centers, where Bangladesh had an astounding 10.5% peak deviation and Uzbekistan had 9.3%. In sharp contrast, weak states are severely supply-side constrained so as to dampen this benefit, with peak export growth of a mere 3.0% in Syria and 3.5% in Afghanistan in the fourth quarter. The conventional monetary policies show a far more limited and temporary quantitative effect on export competitiveness. If the money supply increases by 1 percent, the maximum increase in exports is 0.4 percent in Syria and 2.4 percent in Bangladesh, both of which eventually fade away to zero in the long term. On the other hand, a one percent tightening of the monetary policy rate is sure to squash any export activity, bottoming in the fourth quarter with losses as high as -4.2% in Bangladesh and -3.6% in Uzbekistan. Nevertheless, in cases where monetary expansion is organized at a regional level, the cumulative positive spillovers of the effect approximately increase the export deviations of domestic actions. An integrated growth in the export of Bangladesh is an example whereby a coordinated expansion has a 5.2% export growth in the fourth quarter, which is more than 2.4% produced by an uncoordinated 1% rise in the money supply.

Passing to structural parameters, the only shock that perpetually compounds is an increase in financial development of one standard deviation. Although cyclical shocks revert to 0.0% in the twelfth quarter, financial deepening has maximal quantitative effect in the long run. Countries that are aggressively developing their domestic credit markets are expected to experience significant export growth in the long term. Bangladesh, Uzbekistan and Cambodia are leading the way, with growth rates of 7.2%, 6.4% and 6.2%, respectively. Even in the most severely constrained markets such as Syria and Afghanistan this long-run financial development measure still imposes a positive steady-state deviation of 1.8% and 2.0, respectively. A one standard deviation expansion in domestic credit in the interim is a key quantitative intermediary. This credit shock spurs intermediate fourth-quarter export growth of an average of 1.0% in Syria and 4.8% in Bangladesh which ultimately decays. Another deep-seated structural pillar that has the capacity to lead to huge swings in the volume of trade is the exogenous capital inflow, which is modeled as remittance shocks. Remittances are statistically essential to highly fractured economies, as they produced fourth-quarter export boom of 6.5% in Syria, 6.2% in Yemen and 6.0% in Afghanistan. Such dependence on the diaspora capital brings out a numerical fact that foreign inflows are absolutely necessary to finance the import-intensive production lines of exports.

On the negative side of the ledger, the quantitative models are used in a very graphic manner in terms of the destructive asymmetric punishment of institutional fragility. One standard deviation jump in policy uncertainty is an apocalyptic shock that sends forward-looking export investments into a deep contraction. The data isolates explicitly fragile states, in which this uncertainty provokes fourth-quarter export crashes of -5.5% in Syria, -5.2% in Myanmar and -5.1% in Yemen. This chilling effect even hits structurally sound markets with Bangladesh and Uzbekistan losing 2.2% and 2.4% of their respective export volumes at the peak of the quarter four. These vulnerabilities are further complicated by a one standard deviation shock to the intensity of a shock to energy, which lowers performance in exports across the board by increasing the cost of core production. Since these economies are mainly dependent on energy intensive output, this shock triggers the fourth quarter export losses of between -1.8 in Bangladesh to a devastating -4.6 in Syria and Yemen. There is also a lot of mathematical complexity in the labor market, particularly when it comes to monitoring the informal sector. A 1 percent movement of formal labor to an informal economy is a way of effectively starving the sector that produces exports and this will cause a universal contraction. This dynamic most severely penalizes countries with weak institutional capacity, pushing fourth-quarter export shortages to -3.6% in Syria, -3.4% in Myanmar and -3.2% in Afghanistan. Underdeveloped markets have structural trade friction particular to them, including dependence on non-monetized trade preferences, which makes them even more alien to global supply chains. An increase in the non-monetized trade by one standard deviation moves the transactions out of quantifiable international markets and pulls down formal exports by up to -3.2% in Syria and -2.9% in Yemen. Surprisingly, there is a mathematical anomaly in the analysis of a one standard deviation rise in barter friction shocks. With increasing localized barter difficulty, actors must move into formal monetized markets, which in turn counter-intuitively prompt recorded fourth-quarter exports to increase 2.1% in Syria, and 1.9% in both Yemen and Myanmar.

A rigorous cross-sectional analysis of the time data verifies that, except finance development, the effects of all the shocks modeled fade quickly. The large majority of cyclical perturbations by the twelfth quarter are measuring in a fraction of a percent, which is a strong indication of mean-reverting gravity. As an illustration, the huge 10.5% increase in exchange rate in Bangladesh drops to a mere 2.2% in the quarter twelve and finally, the exchange rate adjusts to 0.0% in the long run. This speedy numeral obliteration strictly restrains the capacities of such transitional economies to alter their export foundation irreversibly by short-run monetary or currency intercessions. This quantitative reality compels policy makers to maneuver through a unique mathematical duality of short-term survival and long-term growth. A 10% currency depreciation is the most statistically sound short-term bailout, creating spikes in yield of double or high single digits in the short term. Nevertheless, to break free from cyclical volatility, uncompromising long-term financial deepening is required — the only modelled measure that can ensure major developing countries achieve steady-state growth of more than 5.0% in the long term. These disaggregated numbers eventually demonstrate that the only way out of such transient and highly volatile export cycles is to broaden formal access to credit and keep the uncertainty down to below a baseline standard deviation.

The remarkable 10.5% peak export growth in Bangladesh is highly plausible and has structural justification, which follows a 10% exchange rate depreciation shock at Quarter 4, thanks to the country’s distinctive macroeconomic features and export composition. The performance is a lot better than that of its regional peers Cambodia, Pakistan and Afghanistan whose domestic economies are vastly different - 9.2%, 7.2% and 3.5% respectively. Bangladesh is taking the lead mainly because of the high elasticity of demand for its exports, which are dominated by ready-made garments that account for 80–85% of total export earnings. Unlike specialised technology or capital goods, mass-market apparel is a highly substitutable consumer good, making global demand for Bangladeshi textiles highly price-elastic. Thus, a 10% depreciation of the Bangladeshi taka immediately causes the foreign currency price of apparel to fall on the global market, making it easy for large retail chains in the US and Europe to shift orders and causing a huge, disproportionate increase in purchases.

Bangladesh’s structural openness and large-scale, cluster efficiencies further enhance this price benefit. Despite having a much larger economy than countries such as Bhutan and Kyrgyzstan, Bangladesh’s modern industrial sector is highly outward-facing. It has large industrial garment clusters with significant underused capacity and labor liquidity, enabling it to rapidly increase output. In stark contrast to Afghanistan or Nepal, whose export is hampered by binding supply-side constraints, landlocked geography or low-elasticity primary commodities such as minerals and selected agricultural goods, India is expected to capture maximum global market in case of shifts in dollar, yen or rupee.

Moreover, Bangladesh’s well-developed backward linkages in spinning, weaving, dyeing and finishing mills mitigate the classic imported inflation traps encountered in traditional Dynamic Stochastic General Equilibrium models. Although depreciation in exchange rate penalizes an economy due to expensive imported raw materials and slows down the export expansion, Bangladesh does most of the value-addition of textile at home. This advantage of the structure means the net competitiveness gain from a weaker currency is much bigger than the inflationary impact of costlier imported raw cotton or machinery. It can thus be concluded that the export surge initially has a positive effect, but that this effect is progressively diminished over time as a result of the long-term price convergence.

Eventually, the gradual staggered shock of first 5.0% to 8.5% in Quarter 2 and 10.5% in Quarter 4 fits well with the micro realities of trade contracts and the J curve effect. Because orders in the global garment industry are generally booked three to six months in advance, the immediate impact shows only limited volume adjustment. By Quarter 4, roughly one year on, supply chains will have fully adjusted and old contracts will have been re-priced. What may take place during Q4 is that international buyers will not limit their supply portfolios to competitor nations such as Vietnam or India and so will make full use of Bangladesh’s competitiveness. For this reason, Bangladesh peaks higher than a highly dollarized Cambodia which diminishes pure exchange rate pass-through or Pakistan who suffers from extreme domestic energy constraints and supply chain frictions.

Whereas frequentist models (e.g., OLS, VAR) are normally used to provide a single point estimate of each model parameter, the Bayesian DSGE models provide an entire posterior distribution, thus capturing ambiguity over the actual values of the parameters (Edge & Gürkaynak, 2010). In this regard, the researchers used the Metropolis-Hastings algorithm - a typical Markov Chain Monte Carlo (MCMC) method - to simulate and draw upon the posterior distribution. Rather than providing a single estimate of the export elasticity, the model produces a probability distribution, which indicates the probability of a set of parameter values, as given in the data from 2000 to 2025. The Bayesian posterior distribution of DSGE parameters is presented in Table 7. The posterior mode provides the most likely value of each parameter whereas the credible intervals show the accuracy of the estimates and the level of uncertainty (Bolker, 2008; Fernández‐Villaverde & Guerrón-Quintana, 2021). The acceptance rate indicates the convergence of the MCMC simulation to acceptable levels raising the confidence in the results. Parameters including monetary policy reaction coefficient (φ1) and the export elasticity with regard to the exchange rate (β1) show somewhat tight credible intervals, which indicate strong estimates. The parameter of risk-aversion (γ) represents a larger credible range, indicating increased uncertainty. The parameters of the output shock (σ₃) and fiscal-policy shock (σ²) have smaller posterior means and, consequently, smaller credible intervals suggesting their less significant impact on the dynamics of the model. The fact that the posterior mean of 0.35 has a very narrow credible interval means that the influence of the monetary policy in the economy is moderately strong and considerably significant. This finding corroborates the hypothesis that fluctuations in interest rates or other monetary instruments exert a quantifiable effect on export performance.

The small confidence interval indicates the extent to which the effectiveness of monetary policy is certain, making it a key tool for controlling economic fluctuations. With an exchange rate posterior mean estimate of 0.425, policymakers can conclude that it has a significant impact on the economy’s openness and competitiveness in international markets. The credible interval indicates that there is some uncertainty, but the overall effect is significant. The exchange rate management, whether by interventions into the foreign exchange markets or by modifying capital controls, can influence export performance and trade balances (Sekkat & Varoudakis, 2000). The estimated posterior of 0.23 indicates a medium sensitivity of exports to exchange-rate changes, which gives important information to the policy makers who may want to stabilize export growth in the presence of changes in exchange rates. The narrow credible interval also adds confidence in this relationship.

The credible intervals of the pertinent parameters are observed in order to assess the temporal propagation of various economic shocks, including monetary policy shocks or fiscal policy shocks, throughout the economy. In reference to the monetary policy shock (σ₁) it is evident that a posterior mean (1.5) coupled with a fairly narrow credible interval indicates that monetary policy shocks (say, changes in interest rates) can bring about quantifiable differences in the economy, for example, an interest rate cut should promote consumption and investment (Juhro et al., 2023). The high acceptance rate indicates strong confidence in this outcome, meaning that policy interventions could be more reliable. Regarding the fiscal policy shock (σ₂), the posterior mean of 0.028 with a narrow 95% credible interval suggests that fiscal policy, government expenditure and tax cuts have a significant impact on the economy. This parameter is particularly useful for determining the effects of expansionary fiscal policy, especially during periods of economic contraction.

In terms of risk management and policy uncertainty, the model can absorb uncertainty, enabling policymakers to tolerate the constant risks associated with any economic intervention. Taking into account the risk-aversion parameter (γ), the posterior mean of 2.45 and the wide credible interval (2.1, 2.7) indicate that there is significant uncertainty surrounding the level of risk aversion in the economy. Central banks use signaling mechanisms to inform markets of policy actions and shape investors’ expectations (Wolski et al., 2023). Regarding the inflation-targeting parameter (π₁), the posterior mean is 1.50, and the narrow credible interval suggests a strong dependence on inflation expectations. This implies that citizens trust the inflation levels set by the central bank. This finding enables policymakers to calibrate inflation-targeting policies and ensure that people’s expectations regarding the central bank’s goals are not overly optimistic.

In order to design long-term structural policies, the posterior mean of export demand sensitivity (η₁) of 0.65, with a narrow confidence interval, indicates that exports are susceptible to external demand shocks, such as changes in global economic conditions or trade policy. Policymakers can use this information to design trade policies that stabilise exports, particularly when global demand fluctuates. Furthermore, the posterior mean of price stickiness (φ₁) of 0.50 indicates a high degree of price rigidity, meaning that prices do not change immediately in response to a shock. This result has significant implications for monetary policy and inflation targeting. In a setting where prices are difficult to change, the response to changes in monetary policy due to inflation may not be instantaneous (Mankiw & Reis, 2002). This is why central banks must consider the time lag between implementing a policy and its impact on inflation.

The fiscal policy shock has a mean of 0.028. This reflects the fiscal constraints faced by frontier economies (Christensen, 2016). Most importantly, the quantitative landscape also changes drastically, when examining the newly integrated structural frictions, which have much larger shock magnitudes than traditional macroeconomic variables do. The informal sector expansion shock is the strongest exogenous shock in the model, which has a tremendous posterior mean of 0.045. This parameter with a credible interval of 90 percent (0.032 to 0.058) demonstrates that the unrecorded shadow economy is an extremely volatile and powerful drain on national resources (Nguyen et al., 2020). This harsh scale measures how fast capital and labor can circumvent the regulatory structures and vigorously deprive the formal manufacturing industry of its productive ability. The barter friction shock is a highly disruptive structural bottleneck in the rural and agricultural sector. With a high posterior mean of 0.038, this is a metric of friction that validates the fact that the search costs relate to matching non-fiat trades are not only widespread but as well as volatile. The credible interval of this barter shock ranges between 0.024 and 0.052, which statistically encapsulate how weak and localized subsistence supply chains fail to scale. Adding to this rural isolation is the non-monetized trade shock that has a significant posterior mean of 0.032. The parameter has a standard deviation of 0.006; this is used to measure the deep-rooted cultural and structural attainment of domestic agents to shun the official fiat currency completely. With the 90 percent credible range between 0.022 and 0.042 being attained in its entirety, it shows the precise extent to which the formal export sector is systematically deprived of the domestic liquidity.

Table 8 reports the disaggregated Bayesian posterior estimates for 16 frontier Asian economies and 90% HPD credible intervals. These disaggregated findings capture the expected structural posterior means for each of the 16 frontier Asian economies, precisely in line with their observed macroeconomic and institutional settings.

Disaggregated Posterior Estimates: The disaggregated posterior estimates irrefutably show that a one-size-fits-all macroeconomic policy is structurally unsustainable for the diverse institutional landscapes of the sixteen frontier Asian economies. Working with the traditional posts in this dynamic stochastic general equilibrium model, remarkable structural differences immediately arise. The inflation targeting coefficient (π₁) shows a clear distinction between economies whose central banks have imploded and those with functional central banks. Countries such as Bangladesh with a posterior mean of 1.75, HPD [1.58, 1.92] and Bhutan with 1.85, HPD [1.65, 2.05] exhibit a strong commitment to the Taylor principle, with parameters far exceeding the crucial threshold of 1.0 to aggressively tackle price fluctuations. By contrast, countries in the midst of civil war, such as Afghanistan (1.15, HPD [1.02, 1.28]) and Syria (1.10, HPD [0.98, 1.22]) reveal inflation targeting parameters close to one, suggesting that their central banks are scientifically impotent to anchor inflation expectations. This monetary impotence is compounded by the wide range of price stickiness (φ₁) observed in the regional panel. For well-behaved transitioning economies such as Bhutan (0.72) with relative stiffness in prices, there is sufficient time for the traditional monetary policies to boost real economic activity before local prices respond. But in highly fragmented markets such as Afghanistan (0.21, HPD [0.15, 0.27]); the price adjustment is hyper-flexible. As a result, any monetary stimulus (σ₁) in these at-risk economies is quickly sterilized by local inflation without benefiting the productive capacity of the economy.

Risk aversion and Exchange Rate Dynamics: The conventional fiscal policy shocks (σ²) also show tremendous variance from Bangladesh’s 0.025 to Syria’s 0.050 reflecting the widely different taxation and public expenditure policies of each country. The risk aversion (γ) coefficient highlights the ingrained risk averse attitudes of local households in an ever changing game of chance that reaches a dangerous level in the case of Syria (3.00) and Afghanistan (2.95). Large risk aversion affects mathematical domestic consumption and investment, and leaves the export sector to bear the burden of economic growth. This proves the importance of the parameters relating to the sensitivity to the exchange rate (α1) and the elasticity of exports (β1). Countries with fledgling manufacturing sectors (e.g. Bangladesh), have a very elastic export response to exchange rate depreciations (1.45, HPD [1.25, 1.65]) - clearly demonstrating that their industrialization can take advantage of low currency valuations in the long term. However, the initial cost of depreciations is still devastating in terms of increasing the cost of imports of raw materials (Dornbusch et al., 1976).

Size of structural frictions: As the policy focus shifts from conventional levers to frictions, the expansion of the informal sector (σ_inf) stands out as a universal virus. This informal economy feeds voraciously upon both labour and capital resources, imposing a hard mathematical constraint on the formal export expansion. Countries with large informal workforces, like Afghanistan and Bangladesh, utilize low-cost informal labor as a key comparative advantage for participation in manufacturing value chains (Narula, 2020). The informality penalty is what explains why money injections do not lead to commensurate increases in global trade. Working alongside the informal work sector is the massive sting of the barter friction shock (σ_bar). In institutionally-fractured or geographically isolated countries such as Afghanistan (0.085) and Papua New Guinea (0.072), vast rural populations completely eschew fiat money in favor of physical barter. This practice of non-standardized bartering creates prohibitive search costs that lock agricultural and mineral surpluses into subsistence-based local exchange. These goods cannot be monetized efficiently and are therefore posited as being beyond the national supply chain necessary for global trade. Moreover, the non-monetized trade shock (σ_nmt) measures a different but equally pernicious preference. When domestic agents choose not to record the value of transactions in the official banking ledger, effectively withdrawing core deposits from the banking system, this can reach critical thresholds in countries such as Afghanistan (0.092) and Syria (0.088). This insidious liquidity crisis erodes commercial banks’ capital base for extending trade finance to formal exporters, which renders domestic firms ineffective on the world stage.

Remittance Paradox and Policy Inferences: Amidst such substantial domestic distortions, the inclusion of foreign remittance inflows (σ_rem) introduces a policy paradox. Remittance shocks provide a much-needed financial buffer, reaching their peak in highly dependent economies such as Tajikistan (0.088, HPD [0.078, 0.098]) and Nepal (0.085, HPD [0.075, 0.095]). This is achieved by directly injecting foreign exchange income into the hands of the poor, thereby lifting domestic consumption. These regular financial transfers temporarily paper over the dysfunctionalities of the national labour market by artificially driving aggregate demand. But the model shows that excessive dependence on these remittances creates extensive moral hazard that erodes local industry. With these sustained transfers of unearned income, households’ reservation wage rises. This effect directly undermines labor supply in the labor-intensive and hardworking low-wage formal manufacturing sector, compounding the brain drain in the informal sector. While remittances ease short-term poverty, they are a structural hindrance to the long-term industrialisation required for export-led growth. The complex dynamics of high risk aversion, pervasive informality and remittance inflows form an economic conundrum that conventional interest rate adjustments cannot cure.

Policies to stimulate these economies through traditional output shocks (σ₃) or price sensitivities (η₁) are lost causes when the transmission mechanisms are cut off by non-monetized transactions. Economic revival in this region requires a radical departure from status quo monetary tweaking. The only area that should receive policy attention is that of deep institutional shocks, which eliminate barter frictions through the information and financial sectors. Shadow economy formalization and diversion of remittance into domestic productive investment are the only quantifiable ways to return policy effectiveness. This structural diagnosis informs us that frontier Asian economies cannot be treated as macroeconomic entities, and require highly disaggregated, quantitative and frictional interventions.

We provide posterior estimates and conversion to convergence diagnostics (Table 9) in order to justify the validity of the DSGE estimates in Table 5. The Brooks-Gelman-Rubin statistic indicates that simulation chains have converged because the values are close to unity (e.g., 1.002, 1.008), far below the critical value, at 1.05, supporting the results of Ting (2025). As a result, the statistical significance represented by the asterisks in Table 2 indicates a consistent posterior distribution rather than noise. This confirms that the Metropolis-Hastings algorithm has achieved successful convergence and that the significance asterisks in Table 2 are not random but demonstrate a fixed posterior distribution. The comparison of high-posterior-density (HPD) intervals with prior means is an internal identification check. For example, the Calvo price-stickiness parameter (θ_p) has a prior mean of 0.50 and a posteriori mean of 0.67. Similarly, the policy-certainty parameter (ρ_POLUNC) has a prior mean of 0.50 and and a posteriori mean of 0.89. These substantive posterior estimates indicate that the empirical quarterly series of 2000Q1 through 2025Q4 of Asian countries have provided meaningfully informative and updated empirics of the DSGE model. It would be a sign of identification failure and therefore invalidate the model if the posterior were equal to the prior values.

The measure of acceptance rate is a proxy for efficiency and is defined as the ratio of accepted parameter draws to the number of proposed parameter draws in Monte Carlo simulations. Table 3 below shows an acceptance rate of 28.4%, comfortably within the optimum range of 20–40% (Geyer & Thompson, 1995). Acceptance rates that are significantly outside the recommended range may indicate inefficient proposal mechanisms, which could lead to slow convergence or poor exploration of the posterior distribution (Ting, 2025). Consequently, the interval and moment sequences were validated as having stabilized at 80,000 draws by performing multivariate convergence diagnostics according to the steps of Brooks and Gelman (1998). This validated the result of the simulation in Table 2.

The inclusion of the informal economy, barter and non-monetized trade in the DSGE offers a pragmatic view of institutional frictions constraining frontier Asia. These three elements differ from the usual cyclical macroeconomic shocks that reflect institutional frictions able to undermine conventional monetary and fiscal policy. The effects of these shocks, their sizes and persistence limits expose structural problems beyond the scope of traditional economic policy. The informal sector stands out as the largest sink of resources in the model, eclipsing the impact of traditional policy shocks. A 1% migration of workers into the informal economy leads to a sharp decline in exports, reaching -0.52% in the second quarter. This implies that the informal economy does not merely serve as a safety net but competes with the formal economy, diverting labor, resources and capital from export-oriented manufacturing. Moreover, the persistence of the shock (with an AR(1) coefficient of 0.85) reveals persistence of informality. Once employed workers and capital move to the informal sector to evade taxes or regulations, it is difficult to reintegrate these workers and firms into the formal sector, which ultimately undermines export capacity.

In contrast, frictions in barter highlight the impact of a hard medium of exchange on village supply chains. The barter shock in terms of search cost leads to a sharp fall in formal exports, with a maximum of -0.22%. This suggests the isolationist nature of barter systems, with local and subsistence forms of trade, and the failure to integrate agricultural or mineral surpluses into national supply chains limits global exports. The shock’s variance of 0.38 highlights the sensitivity of these informal markets to slight variations in their operation. Lack of monetization, where a cultural or structural preference exists to avoid currency, results in a delayed but often more intense liquidity shock. This shock, as opposed to the immediate impacts of barter or informality, has a delayed effect, with a magnitude of -0.35% in the fourth quarter. This lagged pattern casts the steady loss of domestic currency. As more domestic agents shift away from the formal financial system, the banking sector suffers from a lack of capital and is unable to issue letters of credit, trade finance and loans for formal exporters. The persistence of this shock with an AR(1) parameter of 0.81 implies that this form of behavioral risk aversion to the formal currency is deeply rooted, and that institutional change can only be long-term. The cumulative effects of these three factors imply that a central bank can set interest rates or print money, but it will be ineffective if significant proportion of the population is involved in informal trade, barter or non-monetized transactions. The scale of these institutional frictions suggests that solving export stagnation requires more than just monetary policy adjustments. A holistic strategy is required, incorporating financial inclusion, the modernization of rural payment systems and the elimination of regulatory restraints that could stimulate the underground economy.

According to Table 10, the estimated coefficient of 0.384 reflects a very high rate of adjustment at 61.6 percent of any change in total exports out of the long-run equilibrium is being restored in a year. The low value of persistence coefficient suggests that the system is more sensitive to modern shocks. When stochastic changes in the exchange rate reflect undesirable changes, there is an immediate and substantial drop in exports, which highlights the vulnerability of the sector without long-term contractual buffers. Therefore, the evidence suggests that a monetary-fiscal synergy is required. The absence of an integrated policy framework means that the export sector, which has a small internal inertia, is the sector that depends most on external policy stability. The synergistic growth acts as a counterbalance to the stochastic risks of exchange rate volatility, which substantially stabilizes the export sector. In short, the coefficient of the lagged dependent variable at the one percentile level highlights the significance of the 61.6 percent adjustment rate. This suggests that the Asian export sector is characterised by low inertia and high agility, indicating low path dependence, whereby historical values account for less than 18 percent of current outcomes. Contemporaneous macroeconomic fundamentals and shocks are key drivers of export performance, and concurrent monetary and fiscal expansions have an immediate and powerful impact via their transmission channels. Though the interaction between monetary and fiscal policies and improvement of exports seemingly generates almost immediate results, its sustainability depends on the continuity of policy implementation; the flywheel effect is not present, and unless a steady stimulus is applied, the benefits will not automatically be self-renewing. To this end, there is an urgent need to maintain long-term synergistic policy actions to maintain export booms and reduce the high exposure of these economies to stochastic exchange rate changes.

The baseline dynamic specification is confirmed by the positive, statistically significant coefficient on lagged exports, which indicates export persistence in these economies. The friction modifiers radically change the dynamics in ways that reveal the weakened transmission of central bank policy (Idowu et al., 2020). The coefficient of the interaction term between the monetary policy rate and the inverse monetization ratio is strongly positive. This theoretically demonstrates that as the non-monetized barter economy expands, it absorbs the tight monetary policy of interest rate hikes as rural communities ignore fiat money. Likewise, the positive interaction terms in both the monetization ratio and the index of shadow economy demonstrate transmission failure. The informal firms heavily reliant on physical cash flows are largely resistant to formal banking regulations, therefore interest rate hikes do not limit their activities and their funds do not flow back to the formal export sector. As a result, the marginal impact of monetary policy on export growth is weakened in highly informal economies (Idowu et al., 2020). The strictly exogenous structural controls also highlight the bottlenecks in export capacity. The agricultural value-added ratio negatively affects export growth and confirms the region’s subsistence agriculture, rather than high-tech value-added manufacturing. The ratio of the rural population is also a negative determinant in the model, further highlighting that unbanked rural people cannot seamlessly integrate into export supply chains. Moreover, electricity to GDP ratio indicates that power needed to supply the untaxed, unregulated shadow economy hampers the formal export sector’s access to critical physical inputs.

Policy uncertainty is a key disincentive, generally lowering trade volumes by preventing producers from undertaking long-term investment as governance remains in flux (Greenland et al., 2019). Conversely, domestic credit to private enterprises and financial depth have positive effects, confirming that expanding the formal financial sector is vital to support the scaling of global trade (Omri et al., 2015). Government activity exhibits mixed but sensible findings, with productive capital spending marginally contributing to trade infrastructure (Cosar & Demir, 2015) and distortionary taxation increasing shadow economic activity (Medina & Schneider, 2019). Primary energy consumption shows a strong positive coefficient, thus reaffirming that the physical expansion of exporting capacity is restricted by access to primary energy sources and electricity supply stability. This dependency highlights the vulnerability of the region’s export clusters to global commodity price shocks and local electricity shortages. The coefficient of the interaction between the interest rate and broad money supply is significant and positive, suggesting a significant role for liquidity provision. That is, when the central bank heavily raises the policy rate, a simultaneously rising broad money supply helps to grease the shadow financial system and to partially insulate export firms from extreme domestic credit shocks. Financial sector development is also a crucial institutional buffer, as indicated by the strong and positive interaction with the real effective exchange rate.

While simple foreign exchange appreciation would normally devastate export competitiveness, the presence of more developed domestic financial markets enables firms to use more sophisticated hedging and trade finance products to offset these negative exchange rate shocks (Héricourt & Poncet, 2013). By contrast, the interaction between economic policy uncertainty and private sector credit displays a magnified vulnerability. The coefficient is negative, requiring that increased political and regulatory uncertainty completely disrupts the credit transmission channel, making formal credit largely ineffective in funding productive long-term export investments. These detailed econometric dynamics affirm that physical energy infrastructure and advanced financial deepening are essential preconditions to maintaining export growth in times of uncertain domestic policy and erratic currency markets. Intriguingly, the diagnostic statistics corroborate the correctness and empirical plausibility of these intricate mechanisms. By collapsing the instrument matrix, the model uses a reduced set of just 11 instruments for the 16 cross-sectional groups, and fulfils the Roodman rule against instrument proliferation and spurious overfitting. The non-significance of the Arellano-Bond test (A-B AR(2)) and the Hansen J-test in particular decisively rules out any second-order serial correlation and the strict exogeneity of the instruments.

Overall, the DSGE results in Table 5 show that energy consumption negatively affects exports, whereas the panel estimates in Table 8 indicate a positive impact. These seemingly contradictory findings are not in conflict, but rather imply the need to distinguish between the apparently contradictory effects of energy use found in GMM estimations and the findings from DSGE. The GMM results show the unconditional scale effect of energy in the panel S-GMM (0.135) of which this coefficient is positive. Due to the growth in industrial capacity by the Asian economies, the energy consumption and exports increase accordingly. Conversely, the DSGE simulation identifies a conditional cost-efficiency shock. This is an increase in the intensity of energy (or price pass-through) that is not correlated with productivity growth. It increases the marginal cost of production, worsens the terms of trade, and temporarily reduces export competitiveness. The difference between the panel GMM and DSGE results could firstly be explained by the different methodologies and timeframes considered. The DSGE model explains short-term shocks and variations in energy intensity and exports, while the panel GMM reflects long-term empirical relations. These are complementary views: the short-term adverse effect of an energy intensity shock in the DSGE model gives way to a long-term positive effect in the Panel GMM model, as the economy adapts and becomes more productive, achieving export potential through dependency on energy. Since the DSGE outcomes are simulating effects that change in twenty quarters, and the maximum effect is observed at Quarter 5, the short-term adverse effect is an adjustment to an energy shock that could temporarily cause higher costs or inefficiencies. In the long term, the effects of price rigidity and supply-chain limitations are dissipated, which makes the effect neutral or positive. The panel estimation in Table 8 was done to capture the long-term cumulative relationship between energy consumption and exports, with more energy consumption increasing industrial capacity and production efficiency, and thus contributing to higher levels of export development. Accordingly, energy is an essential engine of export capacity over the long run, whereas sudden increases in energy intensity are short-term taxes on export competitiveness.

Table 11 reports the weak identification test results. To test the validity of the instruments used in a GMM/IV estimation framework we also used the Kleibergen-Paap rk Wald F-statistic and compared it with the Stock-Yogo critical values. Our findings show that the Kleibergen-Paap rk Wald F-statistic is 24.85, which is well above the strict Stock-Yogo critical value of 16.38 (for a 10% maximal relative bias). This allows us to reject the null hypothesis of weak instruments. This suggests that we can safely assume that the lagged variables used as instruments in the GMM analysis are strongly related to the endogenous regressors, and that the coefficient estimates are consistent and unbiased.

The most significant outcome of Table 12 is the Instrument-to-Group ratio equal 0.875 that proves the fact that the golden rule of instrument proliferation is followed in the model because it manages to reduce the number of instruments to 11. Stunningly, the diagnostic measures testify to the soundness and empirical soundness of these intricate theoretical processes. The model represents the 16 cross-sectional groups using 11 instruments, achieved by a wise combination of collapsing the instrument matrix, thus passing the Roodman instrument proliferation rule, and avoiding artificial overfitting. This keeps the number of instruments far short of the ceiling of 16 cross-sectional units so that the dynamic panel GMM escapes the traps of too many instruments. In this way, the model safeguards the estimates of the coefficients of complex interactions, including exchange rate dynamics and monetary-fiscal synergy, against skewing by the biases normally present in OLS estimates. However, at the expense of restricting the tools, this study makes sure that the Hansen test maintains its power in statistical terms, and the p-value of 0.413 is very believable. This is a confirmation beyond doubt that the 11 instruments of the study are valid and are strictly exogenous. The p-value of 0.157 in Arellano-Bond AR(2) test proves that there is no second-order serial correlation of error terms in the first difference form. This finding, along with the number of clean instruments, confirms the dynamic specification of the study, which employs lagged dependent variables to effectively model the path dependency of Asian exports. This is a twofold testimony to the mathematical well-being of the model and to the credibility of the findings.

Table 13 is a synthesis of the results that have been provided in Tables 5 and 9; it summarizes the aggregate regional average. Monetary policy and changes in currency are very sensitive to the export engine of Asia whereas monetary infrastructure is a very important stabilizer. A considerably large negative monetary contraction of -0.545 percent would be to suggest that an increase in the policy rate would have a concomitant impact of increasing the costs of credit to manufacturers and likely cause currency appreciation and would make exports more costly to international consumers. The fact that the exchange rate depreciation stands at +0.867 percent confirms that the competitiveness channel, in which currency depreciation reduces the price of goods in foreign countries, is the most dominant in Asia. By implication, a devalued domestic currency puts Asian products at a lower price in the international market, leading to a massive increase in exports. The positive financial growth of +0.445 indicates that deep financial markets (enhanced access to credit, stock-market liquidity, etc.) are not only beneficial but are a core condition in export growth. A negative energy intensity of -0.123 percent is a substantial negative energy impact, reflecting a cost-push inflation, where increasing energy prices are reducing profit margins, but the region seems more robust to this shock than to increases in the interest-rate.

In order to reconcile the +0.867% depreciation effect in Table 9 with the systemic risks within the sixteen poorest Asian countries (such as Cambodia, Laos, Myanmar and Tajikistan), it is necessary to analyze the net effect. Structural weaknesses are common in such economies and they are known to counterbalance the theoretical benefits of a weaker currency (Hina et al., 2023). The benchmark association normally is: Net Export Effect = Price Competitiveness Gain - (Imported Inflation + Debt -servicing costs). In case of the sixteen poorest countries, the equation often provides a declining or negative payoff due to the disproportionate high intensity of energy and debt-to-GDP ratio as compared to the regional average. Although Table 9 shows a high positive coefficient of depreciation (+0.867%), this value indicates the average of all the economies of Asia, including bigger economies like India or Vietnam. In the case of the sixteen poorest nations, the outcome is moderated in terms of:

(1) elasticity pessimism – these countries mainly export primary goods with low price elasticity, so a weaker currency does not necessarily lead to a significant increase in volume (Raza, 2013); and

(2) limited development opportunities. The Asian financial development coefficient (+0.445) is smaller than the regional average, implying a lack of shock absorbers to deal with devaluation volatility.

The limitations of the structures in these economies tend to convert a devaluation policy into a debilitating solution. The risks of devaluation, specifically, the inability to consider inflationary pass-through, foreign-currency debt and financial stability effectively impair the +0.867% coefficient found in the broader sample. This outcome is summarized in Table 14 below. The financial development variable (+0.445) is the shock absorber but in the case of the sixteen poorest countries it is always at the lowest value. Consequently, a scissors effect is created: the benefits of a cheaper currency are limited by the local financial system’s inability to withstand the resulting volatility. In summary, the devaluation strategy is ineffective since the Marshall–Lerner condition, whereby the sum of export and import elasticities is greater than one, is rarely met in these low-complexity economies (Engström, 2017). This is because their export goods do not acquire a substantial market share simply by becoming cheap. Besides, there is inelastic demand for imports, which leads to a worsened trade deficit when currency depreciates (Raza, 2013).

As seen in Table 15, the prevalence of the competitiveness channel (price competitiveness, in which currency depreciation lowers the foreign price of goods) in Asia is validated by the exchange-rate depreciation of +0.867. A depreciated domestic currency makes Asian products more competitive in the international market, thus spurring export volumes (Chit et al., 2008). In interpreting these findings, it is necessary to analyze the transmission process where the competitiveness advantage is balanced by both financial and cost drag pressures. The empirical synthesis presented in Table 11 maps out the complex, countervailing macroeconomic transmission channels that define the net outcome of currency devaluation. Indeed, a weaker currency is a textbook example of being an unconditional stimulus to trade, but as the reconciled results illustrate, once the initial price stimulus is passed, a chain of structural drags begins to crimp the net gains, when trade in today’s Asian context is considered. The basic channel to be considered is the competitiveness channel, in which the explicit currency devaluation reduces the foreign-currency price of domestic products. The price competitiveness advantage offered by a weaker nominal exchange rate is very well confirmed by the large initial depreciation coefficient (+0.867) in baseline estimations, which stimulates purchase volumes from abroad.

But this nominal expansion does not take place in isolation. Inflationary pass-through is a significant headwind for the positive movement in this channel as described in Table 15. Large currency devaluation immediately raises the price of imports of consumer goods and intermediate goods in emerging Asian markets. This pass-through affects import prices significantly, though the impact on the consumer price index varies by country (Ito & Sato, 2008). This quick domestic price increase eventually leads to a leveling off of the real effective exchange rate (REER), which depends not only on the amount of the nominal exchange rate, but also on the rate of domestic-to-foreign price increase. The real, inflation-adjusted price advantage is thus reduced just as quickly as it is created, impeding the long-term growth of export volumes. In addition to price effects, a devaluation of the currency changes the status quo of the domestic supply through the Foreign Currency Debt channel. In the present-day Asian business landscape, manufacturing companies and exporters often access external, dollar-based funding to finance their capital investments and expand their business. If the domestic currency depreciates, the domestic currency value of dollar-denominated debt rises sharply. This creates a negative balance-sheet impact, reducing corporate net worth and investment capacity of the firms with high foreign-currency liabilities (Jiang & Sedik, 2019). Because of the lack of available credit lines for factory expansion or to meet foreign demand, companies have no choice but to divert their limited liquid capital into debt stabilization. This puts financial strain on the companies, reducing their working capital and hindering credit to the private sector, as well as reducing total production capacity (Frankel, 2010). Consequently, even if there is an increase in worldwide demand, Asian producers are structurally unable to increase production to meet it.

The third level of structural resistance is an energy and input cost shock, which can be destructive to domestic production frameworks. Indeed, most manufacturing centers in Asia are closely connected to the global value chain, depending on imports of raw materials, specialized intermediate products and energy, including petroleum and LNG, from outside the country (Baldwin & Freeman, 2022). The impact of currency devaluation on the cost of such essential inputs is even more severe as it is an exponential factor of the country’s basic energy intensity shock. This, in turn, triggers a ripple effect of cost-push inflation throughout the industry. Manufacturing profit margins are squeezed because the prices of raw materials such as electricity, raw cotton, electronic parts and machinery parts are all rising. Exporters are caught in a bind: They cannot increase their export prices in foreign currencies without losing the competitiveness they have just gained, and they cannot cover the rising domestic production costs without going out of business.

Finally, external friction in the transmission process arises from partner reactions and retaliatory trade policies. Fierce currency devaluation by a major Asian exporter is rarely accepted passively by its trading partners or other Asian competitors in the global marketplace. Neighboring countries that are exporting the same basket of goods, that is, products that compete with each other, can neutralize the first country’s advantage in exchange rates by engaging in competitive devaluations (Rodnyansky, 2018). The major importing nations of the west could counter the massive inflow of cheap imports by imposing anti-dumping duties, safeguard tariffs or non-tariff technical barriers to safeguard their domestic sectors (Jabbour et al., 2018). These responses in a game-theory fashion freeze out the shift of the export demand curve to the outside. Consequently, the demand for the export of the devaluing country quickly levels off and devaluation is unable to produce long-term economic growth. The results thus serve as a reminder that devaluation can only achieve nominal gains, which are invariably subject to internal cost-push pressures and external structural constraints.

Table 16 provides a subsample comparison of heterogeneity outcomes using GMM estimation. Due to the differences in economic structures within the sample, the data is divided into Group A (Emerging Markets) and Group B (Low-Income/Fragile Economies). These findings point to structural imbalance. The interest-rate channel is more effective in Emerging Markets, where credit channels work well, but less in low-income countries (-0.056) because of high informality and limited access to banking. Countries with low incomes are more vulnerable to currency fluctuations (0.312) as their exports are usually low-value commodities that are price-elastic as compared to emerging markets, which export more sophisticated products. The damaging effects of policy uncertainty on fragile economies are three times greater than on emerging markets (-0.345).

The empirical findings of the subsample comparison of heterogeneity estimation show deep and structurally-driven differences between emerging Asian economies and low-income and fragile economies, which provide a valuable understanding of the effects of structural vulnerabilities on the macroeconomic transmission mechanism. The common-size negative coefficients obtained in all three samples confirm the general validity of the standard open-economy economic theory, which predicts that the domestic currency’s real appreciation will reduce international competitiveness and slow export expansion all over the continent (Nucci & Pozzolo, 2010). In terms of magnitudes, the overall sample of Asian countries shows a notably high elasticity of REER of the order of -0.215, a value which gauges the impact of currency pricing on regional trade. Disaggregated, Subsample A (India, Vietnam and Indonesia) shows a more robust REER coefficient of -0.174, suggesting that a regime of real appreciation does hurt exports, but not too much. This relative insulation is closely associated with their strong ties to the rest of the world, as evidenced by their high export persistence coefficient of 0.268, which means that these countries have robust global supply chain relationships, long-term trade contracts and non-price brand equity. Consequently, international buyers face considerable challenges in discontinuing their purchases when local currencies appreciate. By contrast, the export sector of Subsample B, which includes low income and fragile countries such as Nepal, Afghanistan and Myanmar, has a REER coefficient of -0.298, almost double that of its emerging market counterparts. Its export persistence value of only 0.064 (statistically insignificant) indicates that fragile economies have little sophistication in commercial networks, industrial diversification nor market power to withstand export price shocks and remain reliant on highly price elastic primary commodities. This localised effect is well captured by the positive and significant interaction term coefficient between exchange rates and financial depth, which is shown in Table 8 to be 0.105. From an econometric perspective, this positive interaction modifier acts as a strong negative offset, reducing the initial negative effect of the appreciation shock.

Local export firms have access to better financing options, such as credit facilities, hedging tools and trade financing. These options are better able to mitigate currency shocks and ensure supply chain continuity. This is due to the deep financial systems in emerging markets, which have a high and statistically significant financial development coefficient of 0.485. In comparison, the fragile countries in Subsample B have very shallow domestic credit channels, with a very low and statistically insignificant financial depth coefficient of 0.105. These low-income economies do not have a robust financial ecosystem to provide a buffer for real appreciation or to support them when they face tight monetary conditions. Consequently, the baseline monetary policy rate transmission in these economies is negligible at -0.042. Finally, the status of fragile states without a financial buffer makes them fully vulnerable to the unabated impact of currency fluctuations and additional institutional risks, as is reflected in their extremely high policy uncertainty coefficient of -0.362. This establishes a vicious circle: if there are no developed financial markets and robust supply chains, the economy’s macroeconomic instability negatively affects trade growth.

To ensure structural consistency across the empirical frameworks, the baseline REER results in Table 9 must be interpreted in tandem with the conditional subsample analysis in Table 16. In both specifications, the baseline REER coefficient retains its theoretically expected negative sign (-0.142 in Table 9 and -0.215 in Table 16), validating the premise that real effective exchange rate appreciation harms international price competitiveness and dampens Asian export growth (Berthou, 2008). The apparent shift in magnitude between the full sample benchmark in Table 16 (-0.215) and the complete model in Table 9 (-0.142) is directly explained by the inclusion of the financial development interaction modifier (REER×FINDEV = 0.105**). This interaction term shows that the total marginal effect of an exchange rate shock depends on the level of domestic financial depth. This transmission logic seamlessly explains the cross-subsample heterogeneity observed in Table 16. In emerging markets (Subsample A), where financial development is robust and highly significant (0.485***), the net negative impact of appreciation is heavily attenuated to -0.174 due to active corporate hedging and access to trade credit channels. Conversely, in low-income and fragile states (Subsample B), where the financial ecosystem is structurally weak and statistically unviable (0.105), the institutional buffer is removed from the transmission mechanism. Consequently, fragile export sectors are completely exposed to currency volatility, causing their baseline real REER sensitivity to amplify catastrophically to -0.298.

The subsample analysis in Table 17 reveals a significant structural difference: while emerging markets exhibit the expected response to monetary policy instruments, low-income economies are relatively immune to changes in interest rates, but are more susceptible to political uncertainty and energy cost shocks. Consequently, traditional inflation-targeting regimes are characterised by a low degree of effectiveness in the poorest countries in the region.

To determine the strength of our results and rule out outliers such as Vietnam’s abnormal growth path or Afghanistan’s excessive volatility, we performed further subsample stability tests. The results are reported in Table 18. They confirm the presence of underlying economic rationality in heterogeneous low-income settings. Additionally, by assessing the performance of the model on two structurally different subsamples, we proved that the directional validity of the key transmission mechanisms is not lost, thereby contributing to the overall relevance of the findings.