Economic landscape of Pakistan is determined by an extremely complex interaction of domestic and global forces; navigating it successfully requires a clear understanding of its character. The paper explores the dynamic relationships between macroeconomic variables and GDP growth in Pakistan using the Autoregressive Distributed Lag (ARDL) model and other stability tests using time series data from 1980 to 2022. The analysis includes variables representing GDP per capita, inflation, imports, total debt as a percentage of GDP, total population, and forestry and agricultural output. The correlation matrix shows a positive association between GDP growth rate and GDP per capita, total debt service is inversely correlated with total population, and GDP demonstrates a significant negative correlation. The ARDL results indicate that GDP per capita and the agriculture and forestry sectors are significant drivers of economic growth. Over the period in question, inflation only marginally affected GDP growth showing how important it is to maintain price stability through effective policies. Imports provide short-term benefits by enhancing productivity through capital goods and technology inflows but they may pose long-term challenges due to trade imbalances. The influence of population growth appears to be ambivalent: in the short term it contributes to economic growth by increasing labor supply and consumption; in the long term, however, its effect may become detrimental owing to resource constraints. Public debt shows little influence in the short term but negatively impacts growth over time by increasing the fiscal burden of debt servicing. These findings suggest that to achieve long-term economic stability and growth, the country needs targeted policy interventions that should help it control inflation, manage the debt sustainably, optimize imports, and invest in agriculture, which is an important determinant of GDP growth. Future research should concentrate on sector-specific studies and the effects of political stability on economic growth in order to provide deeper insights contributing to Pakistan’s sustainable economic development.

Macroeconomic Dynamics, Economic Stability, GDP Growth, Inflation, Agriculture and Forestry, ARDL Model, Pakistan

The economy of Pakistan, with its complex economic landscape and serious potential for growth and development, has been subject to various forces, both domestic and global. The most pressing issues the country has been grappling with over the past decades include high inflation, energy shortage, water stress, and fiscal instability (Sherani, 2008). Pakistan’s economic progress is also impeded by occasional inflation spikes, trade deficits, and low literacy levels (Sohail et al., 2023). The country needs comprehensive reform strategies to stabilize and then propel its economy towards sustainable growth and socioeconomic resilience. The macroeconomic dynamics of Pakistan’s economy are determined by factors related to business cycles and long-term growth, trade balance, external debt, GDP deflator, and FDI (Khan & Jawed, 2019). The government of Pakistan also has to address various challenges like unemployment, inflation, and energy crisis to achieve sustainable growth (Zaheer et al., 2022). The interdependence between macroeconomic policies and industrial performance together with other macroeconomic factors significantly impacts Pakistan’s manufacturing sector, private investment, growth, and competition among corporations (Mangla & Din, 2015).

Pakistan’s economy has experienced significant fluctuations over the past four decades driven by a mix of domestic and international factors. There have been periods of economic growth but persistent challenges like inflation, external debt, trade deficits, and population pressures continue to hinder the country’s sustained development. The existing literature mostly focuses on isolated variables or shorter timeframes so a critical gap remains in understanding how these macroeconomic variables together influence GDP growth over an extended period. Moreover, the bidirectional effects of population dynamics and contributions of certain key sectors, e.g. agriculture and forestry, to economic performance appear to be underexplored. This study aims to address these gaps by employing an integrated approach to the analysis of the dynamic relationships between Pakistan’s GDP growth and other macroeconomic variables. Using ARDL model, it investigates their short-term and long-term impacts and offers comprehensive perspective on the factors that drive or impede economic stability and growth. The findings should provide actionable insights for policymakers to formulate effective strategies that will promote sustained growth and enhance resilience against future economic challenges.

This research aims to investigate macroeconomic factors that influenced GDP growth in Pakistan between 1980 and 2022, including GDP per capita, imports, inflation, population growth, and public debt. One of the primary objectives is to assess how these variables interrelate to shape economic performance. The study evaluates the distinct short-term and long-term effects of targeted variables on economic growth and, based on the empirical findings, outlines possible measures for promoting sustainable economic growth in Pakistan. The research intends to answer the following key questions:

What were the major determinants of GDP growth in Pakistan over the last four decades? How did these factors affect the economic landscape? What were the differential impacts of inflation, agricultural output, and public debt on GDP growth in the short and long run? and how effectively does the ARDL model explain the interactions between these variables and their influence on economic growth?

The study hypothesizes a positive relationship between GDP per capita and GDP growth, suggesting that higher income levels contribute to broader economic expansion. This hypothesis is supported by empirical results indicating that GDP per capita significantly boosts economic growth. Inflation is expected to have a negative impact on GDP growth as high inflation erodes purchasing power and results in economic instability. The findings confirm that inflation negatively affects GDP growth, especially in the long run. The role of imports is hypothesized to be ambivalent. While imports can positively impact GDP growth by bringing in capital goods and technology, excessive reliance on imports may lead to trade deficits. The results indicate that imports have mixed effects, boosting growth in the short term but posing risks if not managed properly over time. Population growth is anticipated to a have bidirectional impact. It can enhance labor supply and consumption but unchecked growth may strain resources and infrastructure. The analysis reveals that population growth has a significant effect on GDP, with both positive and negative implications depending on the context. Public debt is hypothesized to have a detrimental impact on GDP growth due to the financial burden of debt servicing and subsequent reduction in fiscal space for productive investments. The research results confirm this hypothesis showing that high public debt, especially in the long run, adversely affects economic growth, which highlights the importance of effective debt management for sustained economic stability. By testing these hypotheses, the research provides a deeper understanding of the macroeconomic dynamics in Pakistan and offers valuable insights for policymakers to develop strategies that balance growth with economic stability.

Historically Pakistan’s economy has been marked by volatility, with periods of growth followed by stagnation or decline because of fiscal deficits, inflationary pressures, and trade imbalances. Previous studies have highlighted a significant impact of inflation, trade openness, and foreign direct investment (FDI) in shaping economic outcomes (Mansoor & Bibi, 2018; Bibi et al., 2014). However, such studies often lack an integrated approach that allows researchers to examine the simultaneous interaction of multiple macroeconomic variables over an extended period. The present paper addresses this gap by offering a comprehensive analysis spanning four decades, which captures the evolving nature of Pakistan’s economy and provides a robust basis for policy guidelines. The ARDL model is particularly advantageous in this context as it allows for the distinction between short-term adjustments and long-term equilibrium relationships. This is crucial for policymakers who need to understand not only the immediate effects of economic policies but also their sustained impacts. Inflation, for instance, may have an immediate adverse effect on economic growth but its long-term consequences can be mitigated through sound monetary policies. Similarly, the relationship between imports and GDP growth is complex; while imports of capital goods can spur growth by enhancing productivity, excessive reliance on imports can lead to trade deficits and economic vulnerability.

Population dynamics, agriculture, and forestry are often critical yet underexplored areas in economic growth studies. Population growth, for instance, can impact the economy in two ways: it can drive growth through increasing labor force and consumer base but it can also strain resources and infrastructure if not managed properly. The study’s insights into the bidirectional nature of its impact on GDP may prove valuable for formulating demographic and economic policies. For an agrarian economy like Pakistan, where a large share of the population depends on agriculture for livelihood, to achieve sustainable growth, it is absolutely essential to understand how this sector interacts with macroeconomic variables. The findings suggest that enhanced agricultural productivity and sustainable forestry practices can have a positive ripple effect on the whole economy, contributing to food security, employment, and export revenues. The study’s conclusions on how to control inflation, optimize debt service, and foster export-oriented industries are grounded in empirical evidence, which should make them useful for resolving Pakistan’s economic challenges. Future research directions suggested by the authors, such as sector-specific analyses and assessing the role of political stability in economic growth may become valuable contributions to economic studies and their potential to inform policy discourse in Pakistan.

The paper offers a thorough analysis of the macroeconomic factors affecting GDP growth but it is not without limitations. One key limitation is the reliance on secondary data sourced from international databases. Although these sources are generally reliable, discrepancies or gaps in their quality and availability could affect the robustness of the results. Moreover, the study covers a broad timeframe, and structural changes in the economy or external shocks such as global financial crises may not be fully accounted for in the model. Another limitation is the paper’s focus on quantitative analysis: it may give insufficient attention to some of the qualitative factors, e.g. political stability, governance quality or institutional effectiveness. These elements play a critical role in economic performance but may be difficult to quantify and integrate into econometric models. Additionally, the ARDL model, while suitable for capturing short-term and long-term relationships, has inherent limitations related to lag selection and potential model specification errors, which could influence the results. Lastly, the study is context-specific, which limits the generalizability of the findings to other developing countries with different economic structures.

The nexus between macroeconomic variables and economic growth has long intrigued economists and policymakers, especially in developing economies such as Pakistan. This section offers an overview of the key studies into this relationship within Pakistan’s economic context, highlighting significant results and areas that require further investigation. Bibi, Ahmad, and Rashid (2014) conducted a comprehensive analysis of the effects of trade openness, foreign direct investment (FDI), exchange rates, and inflation on Pakistan’s economic growth from 1980 to 2011. Employing unit root tests, cointegration techniques, and dynamic OLS regression, they found that imports, exports, and FDI positively influenced economic growth, although the impact of FDI was not statistically significant. Conversely, inflation and trade openness were shown to have negatively affected growth, with trade openness particularly harmful because of exchange rate depreciation and high import levels. Their research sheds light on the key macroeconomic factors affecting Pakistan’s growth and provides valuable empirical evidence for policy-making aimed at promoting sustainable development. The paper also outlines areas for future investigation, such as the need for more precise policy interventions. In a later study, Mansoor and Bibi (2019) expanded the scope of analysis by exploring the relationships between inflation, exchange rates, FDI, and GDP in Pakistan in 1980-2016. Using unit root tests and ARDL regression analysis, they examined both bidirectional and unidirectional relationships between these variables. The results pointed to significant relationships in both long and short term; FDI and the real exchange rate (REF) positively influenced GDP in the short term. However, the link between GDP, the real exchange rate, and inflation was weaker. The study confirmed the stability of the model at a 5% significance level and enriched the understanding of the dynamic interplay between these macroeconomic factors, building on the findings of Bibi, Ahmad, and Rashid (2014).

Further exploring macroeconomic dynamics, Jadoon and Guang (2019) examined the effects of exchange rate fluctuations on Pakistan’s trade balance, using annual time series data from 1971 to 2016. Their regression analysis focused on the relationships between the exchange rate, money supply, inflation, and the trade balance. These findings point to the importance of policy measures that can improve the trade balance and facilitate economic growth, complementing research on Pakistan’s macroeconomic dynamics. Shaikh, Channa, and Bhutto (2019) added to this body of knowledge by examining the relationship between the exchange rate and selected macroeconomic variables—imports, GDP, inflation, and exports—from 1992 to 2017. Their study employed secondary data and multiple regression models and found that imports and GDP had a significant negative impact on the exchange rate, while exports and inflation had a significant positive impact. The study suggested that government policies should focus on increasing production, promoting exports, reducing imports, and maintaining price stability to enhance the effectiveness of the exchange rates policy. This research provided further insights into the relationship between exchange rate and other macroeconomic variables in Pakistan. Khan, Zubair, Ullah, and Talal (2023) investigated how foreign debt influenced Pakistan’s economic growth between 1990 and 2020, using dynamic ADL models and Granger causality tests. Their research revealed that economic growth in Pakistan was notably impacted by debt servicing, and fluctuations in exchange rates. Unlike earlier studies, their analysis detected no direct causal link between GDP and foreign debt. The researchers challenged the previous assumptions about the relationship between foreign debt and GDP showing that foreign debt adversely affected economic performance through inflation, exchange rate volatility and debt servicing challenges; they offer a deeper understanding of Pakistan’s debt crisis and its effects on economic growth. In another study, Irshad, Hussain, and Baig (2022) examined the effects of foreign direct investment (FDI), trade volume, and exchange rates on Pakistan’s GDP from 1972 to 2021. Their regression analysis showed that FDI had a positive effect on GDP, whereas exchange rate changes influenced it negatively. Additionally, the trade balance was found to have an insignificant impact on GDP. Their findings mean that Pakistan’s economy needs a supportive environment for foreign investment and effectively managed currency fluctuations to promote economic growth, which reinforces broader themes in macroeconomic research concerning the country. In a more comprehensive analysis, Meraj, Ahmed, and Abbas (2024) examine how exchange rate fluctuations affect the key economic indicators in Pakistan, including GDP growth, inflation rates, FDI, CPI, trade balances, and export and import levels. By employing correlation, regression, and other analytical tools, the study uncovered the intricate relationships between exchange rate fluctuations and various economic indicators, emphasizing their implications for Pakistan’s economic performance and trade dynamics. Their research contributes valuable insights into the specific impact of exchange rate fluctuations and thus furthers the understanding of Pakistan’s macroeconomic framework. Ghauri et al. (2024) identify the key factors driving exchange rate volatility in Pakistan and assess their impact on economic sustainability. Their analysis, based on the ARDL model and Granger causality tests, reveals that factors such as debt, interest rates, and political stability significantly influence currency volatility. The terms of trade, amounts of debt, political instability, and inflation can also increase exchange rate volatility, which in turn negatively affects Pakistan’s current account balance. Mehak and Waqas (2023) investigated the effects of inflation and foreign direct investment (FDI) on economic growth in Pakistan, analyzing time series data from 1973 to 2020. They employed Consumer Price Index (CPI) to represent inflation and discovered a positive long-term relationship between both inflation and FDI on the one hand and economic growth on the other. The study found that the highest rates of economic growth occurred when inflation was kept below 2.80 percent. While FDI had a beneficial impact on growth at lower levels of inflation, its effect turned negative once inflation surpassed this threshold. Ahmed et al. (2018) investigated the relationship between inflation and exports/imports in Pakistan using monthly data from 2001 to 2017. Their error correction and cointegration models, along with Granger causality analysis, showed that a 1% increase in exports/imports led to a 0.63%/0.57% rise in inflation. The study’s variance decomposition revealed that exports had most the significant impact on inflation. Nawaz et al. (2017) examined the correlation and causality between inflation and selected macroeconomic variables in Pakistan from 1990 to 2012. The study is based on empirical evidence; it utilizes regression analysis, correlation coefficients, and Granger causality tests to confirm positive associations between inflation and several macroeconomic variables. Results from regression analysis indicate that money supply, government expenditure, government revenue, foreign direct investment, and gross domestic product have a positive impact on inflation in Pakistan, while interest rate shows a negative impact. Naeem (2021) focused on the influence of the exchange rate, interest rate, and economic conditions on FDI in Pakistan from 1972 to 2019. Using time series data and applying unit root tests and ARDL cointegration analysis, the study found that economic conditions and political instability have a negative and insignificant impact on FDI. Conversely, the exchange rate exhibited a positive and significant relationship with FDI, while exports had a negative but insignificant impact. Interest rates and GDP also showed a positive and significant relationship with FDI. The study highlighted the need for further exploration of factors affecting FDI, particularly economic and political conditions. Agriculture and forestry are essential to Pakistan’s economy as they substantially contribute to GDP, employment, and export revenues. These sectors are crucial for ensuring food security, supplying raw materials, and promoting environmental sustainability. Research into these areas can shed light on how advancements in agricultural productivity and sustainable forestry practices affect overall economic growth. Improving efficiency in these sectors can boost economic output and enhance livelihoods, especially in rural communities. Understanding the interactions between agriculture, forestry, and other macroeconomic factors can help to design comprehensive policies for sustainable development. Population growth is a fundamental variable that influences labor markets, consumption patterns, and economic growth. Changes in population dynamics can to a high degree determine economic policies and growth trajectories. Integrating population variables into economic analyses can offer a more holistic view of growth potential and challenges facing the economy. The examined literature provides a thorough overview of various relationships between macroeconomic factors and economic growth in Pakistan. However, there is a need for research that will take into account GDP rate, CPI, GDP per capita, total debt service, and population dynamics. The contribution of agriculture and forestry to economic growth should also be further explored. By addressing these gaps, future studies can offer deeper insights into the complex dynamics at play, enabling policymakers to formulate more effective strategies for sustained economic growth, manage trade deficits and inflation, and leverage positive factors like imports, exports, and FDI for Pakistan’s economic development.

The ARDL model is a widely used tool of analysis (Ghauri et al., 2024; Khan et al., 2023). It proves particularly effective in capturing both long-term and short-term dynamics and so is an ideal choice for analyzing the intricate relationships between macroeconomic variables and growth. The Autoregressive Distributed Lag (ARDL) model introduced by Pesaran and Shin (1995) has become a popular econometric method for analyzing dynamic relationships between variables, particularly in macroeconomic studies. One of its principal advantages is its flexibility in accommodating variables integrated at different orders, specifically I(0) and I(1), without the need for pre-testing the order of integration. It is particularly useful for economic time-series data that often include variables with mixed integration orders, thus reducing the risk of pre-test biases and ensuring a robust analytical framework (Nkoro & Uko, 2016; Haug, 2002). The ARDL model also facilitates the estimation of short-run and long-run relationships in a single equation framework providing a comprehensive view of dynamic interactions between the variables. This dual estimation capability is critical for policymakers who need to understand not only the immediate effects of economic shocks but also their long-term implications. The ARDL algorithm ability to separately estimate these effects enhances its utility in crafting effective economic policies (Pesaran et al., 2001; Salisu & Isah, 2017).

Another significant advantage is its good performance in small sample sizes, which makes it particularly suitable for studies with limited data, a common scenario in developing economies where long-term historical data may not be reliable or even available. The ARDL model produces reliable and consistent estimates, even when the sample size is not large enough to meet the asymptotic properties required by other cointegration techniques such as the Johansen approach (Narayan, 2005; Bahmani-Oskooee & Bohl, 2000). Furthermore, the ARDL model incorporates lags of both the dependent and independent variables, capturing the adjustment dynamics more effectively. This feature is particularly valuable in macroeconomic contexts where the effects of policy changes or external shocks on economic indicators may be delayed. The inclusion of lagged variables helps identify the speed and path of adjustment toward long-run equilibrium and offers insights into the persistence of economic phenomena (Laurenceson, 2003; Duasa, 2007).

Yet, the ARDL model has its limitations. One critical issue is the potential for misspecification, especially in the selection of appropriate lag lengths. The accuracy of ARDL estimates heavily depends on the correct identification of lag lengths for each variable since improper selection can lead to biased or inconsistent results. This challenge necessitates a precise application of lag selection criteria such as the Akaike Information Criterion (AIC) and Schwarz Bayesian Criterion (SBC) to ensure model reliability (Lütkepohl, 2005; Ahmad & Du, 2017). Another concern is the assumption of error term properties particularly needed for serially uncorrelated and homoscedastic residuals. Violations of these assumptions can lead to inefficiencies in the parameter estimates and unreliable testing of hypotheses. Thorough diagnostic checks, including the Breusch-Godfrey test for serial correlation and the Breusch-Pagan test for heteroscedasticity, are crucial in validating the robustness of the model (Gujarati & Porter, 2009; Shahbaz et al., 2015) . While the ARDL model is proficient at identifying long-run relationships it does not inherently establish causality. The observed correlations between variables may still be subject to endogeneity issues where explanatory variables are correlated with error term, therefore challenging the causal interpretation of the results. To address this difficulty, researchers often complement the ARDL findings with additional causality tests like Granger tests or use instrumental variable approaches to provide a more rigorous causal analysis (Enders, 2008; Jalil & Ma, 2008). Furthermore, the reliance on bounds testing for cointegration in ARDL framework has been critisized for its sensitivity to the choice of critical values and sample size. The bounds test may yield inconclusive results if the computed F-statistic lies between the upper and lower critical values necessitating a more cautious interpretation of the cointegration results (Narayan & Narayan, 2006; Turner, 2006).

Recent literature has seen an increase in the applications of ARDL models in diverse economic contexts reflecting its adaptability and relevance. For instance, studies on the impact of exchange rate volatility on trade balances, inflation dynamics and the nexus between energy consumption and economic growth have all employed the ARDL framework to provide nuanced insights into these complex relationships (Xu et al., 2018; Aye et al., 2014). Advancements in econometric software have facilitated the broader adoption of ARDL models by simplifying the estimation process and providing more reliable diagnostic tools. These improvements have enhanced the accessibility and accuracy of ARDL applications and enabled more sophisticated analyses of economic phenomena. Overall, the ARDL model is a powerful econometric tool for analyzing the dynamic relationships between macroeconomic variables; it has significant advantages in terms of flexibility, dual estimation capacity and suitability for small sample sizes. Yet, its application requires careful consideration of potential limitations, particularly regarding lag selection, residual diagnostics and interpretation of causality. By addressing these challenges through rigorous methodological practices and complementary analyses, researchers can leverage the ARDL model to derive robust and policy-relevant insights into complex dynamics of economic systems.

The ARDL model is specified as follows:

$\mathrm{\Delta }GD{P}_{-}RAT{E}_t=a_0+\sum _{i=1}^p{ß}_i\Delta GD{P}_{-}RAT{E}_{t-i}+\sum _{j=0}^q{\gamma }_j\Delta X_{t-j}+\phi G$

Selecting the optimal lag length is essential for capturing the true dynamics of the variables without overfitting the model. The VAR Lag Order Selection Criteria including Akaike Information Criterion (AIC), Schwarz Criterion (SC), and Hannan-Quinn Criterion (HQ) are used to determine the appropriate lag structure for the ARDL model. Among these, the AIC is often preferred for its ability to balance model fit and complexity especially in small samples (Lütkepohl, 2005). The chosen lag length is crucial for ensuring that the model accurately reflects the underlying economic processes. The ARDL bounds testing approach is chosen to test for the existence of a long-run relationship among the variables. This method involves comparing calculated F-statistic with critical values to determine if cointegration exists (Pesaran et al., 2001). The bound’s testing approach is particularly versatile and robust because it does not require all variables to be integrated in the same order when analyzing long-run relationships in time-series data. Upon confirming the existence of cointegration, the ARDL model is used to estimate both short-run and long-run dynamics of relationships between the variables. The short-run coefficients reveal the immediate impact of changes in independent variables on GDP growth, while long-run coefficients provide insights into equilibrium relationships. This dual analysis promotes a better understanding of how macroeconomic factors influence GDP growth over different time horizons (Pesaran & Shin, 1995).

The long-run equation can be expressed as:

$GD{P}_{-}RAT{E}_t=\mu +\sum _{k=1}^n{\delta }_k{X}_{t-k}+u_t$

To ensure the validity of the ARDL model, various diagnostic tests are conducted to check for autocorrelation, heteroscedasticity and model stability. These tests are essential for verifying the reliability of the model’s estimates and ensuring that the results are not driven by violations of key econometric assumptions (Gujarati & Porter, 2009). Robustness checks are performed to confirm the consistency of the findings by estimating alternative model specifications, including different lag lengths and excluding trends.

The paper contributes to the existing research into the interplay between macroeconomic variables and economic growth in Pakistan, shedding light on previously underexplored areas. The results are contextualized within a broader body of research in the current section highlighting both consistencies and deviations from established studies, and discussing their implications for policy.

The analysis demonstrates that GDP growth in Pakistan is significantly influenced by several key macroeconomic factors, including GDP per capita, inflation (CPI), imports, and the Agriculture and Forestry (A&F) sector. These findings are in line with previous research, particularly the work of Mansoor and Bibi (2019) and Bibi, Ahmad, and Rashid (2014), who identified inflation and trade openness as critical determinants of economic growth. However, this study brings to the forefront the substantial positive impact of the A&F sector on GDP growth—an area that has not been sufficiently emphasized in prior research. While earlier studies, such as those by Shaikh, Channa, and Bhutto (2019), primarily focused on trade and exchange rates, the current findings underscore the vital role of the A&F sector. Given its significance in terms of direct GDP contribution, employment, and export revenues, these empirical results reveal that economic growth is much improved by policies focused on boosting agricultural productivity and implementing sustainable forestry practices.

The study’s findings on inflation align with the conclusions drawn by Mehak and Waqas (2023), who observed that economic growth benefits from controlled inflation, particularly when it remains below a certain threshold. The present study reinforces the notion that a properly managed inflation can have a positive effect on economic growth. These findings do not support the earlier work of Bibi, Ahmad, and Rashid (2014), who considered inflation to be always detrimental to growth. This difference in opinion may result from specific economic contexts that determined the impact of inflation on economic growth in Pakistan in different periods of its recent history.

The positive impact of imports on GDP growth identified in this study is consistent with the findings of Bibi, Ahmad, and Rashid (2014), who noted that imports, along with exports, contribute positively to economic expansion. The current study suggests that the growth-enhancing effect of imports may be caused by the inflow of capital goods and technology, which enhance productivity and innovation, further stimulating economic growth.

In contrast to studies such as Khan et al. (2023), which emphasize the negative impact of external debt due to the burden of debt servicing, this study finds that total debt service becomes significant only after differencing, indicating its relevance primarily in the long run. This suggests that while debt servicing presents a financial burden, its immediate effects on GDP growth may be limited or contingent upon the use of borrowed funds and the effectiveness of broader economic management strategies.

The population growth significantly impacts economic growth, as revealed in the study, though its effects become evident only after first differencing. The empirical results support economic theories that posit population growth to be a driver of economic activity through increased labor supply and consumption, though these effects may unfold over time rather than manifest immediately. This observation is consistent with the literature that underscores the role of demographic shifts in shaping long-term economic growth trajectories, as suggested by Naeem (2021).

The pronounced significance of the Agriculture and Forestry sector in this study underscores its critical role within Pakistan’s economic landscape, supporting the argument that A&F sectors are essential for achieving sustainable economic development. The study highlights the need for incorporating sectoral analyses into broader macroeconomic models, given that the prevailing body of the existing literature mostly emphasizes trade-related and industrial variables. Such an approach is necessary to fully understand the dynamics of growth in an agrarian-based economy like Pakistan. Future research should continue to explore these interrelationships to provide a more comprehensive understanding of the drivers of economic growth.

This study offers important insights into the determinants of economic growth in Pakistan, focusing on the impact of key macroeconomic variables such as inflation, imports, GDP per capita, total debt service, population growth, and the Agriculture and Forestry (A&F) sector. Using the Autoregressive Distributed Lag (ARDL) model, the research finds that inflation and imports have a positive effect on GDP growth, while the impacts of debt service and population growth are more nuanced. The significant role of the A&F sector implies the need for targeted policies to harness its potential for driving economic development. The results support the findings of existing literature, particularly regarding the importance of trade openness and foreign investment, while also offering a brand-new perspective on the complexities surrounding debt service and population dynamics. The literature on the nexus between macroeconomic variables and economic growth in Pakistan reveals a complex interplay of factors, each exerting significant influence on the country’s economic trajectory. Studies such as those by Bibi, Ahmad, and Rashid (2014) and Mansoor and Bibi (2019) underscore the positive impact of FDI and trade-related factors on economic growth, albeit with varying degrees of statistical significance. Meanwhile, inflation and exchange rate volatility emerge as critical determinants with mostly negative effects on economic growth, as highlighted by research from Jadoon and Guang (2019) and Shaikh, Channa, and Bhutto (2019). To bolster economic stability and growth, policymakers should prioritize several key areas. Control over inflation through stringent monetary policies and fiscal discipline is essential for stabilizing the economy and enhancing investor confidence. Promoting export-oriented industries and regulating imports will help manage trade deficits and support GDP growth. Effective debt management through optimized debt servicing and increased revenue generation is vital for mitigating the adverse effects of high debt levels. Investment in the Agriculture and Forestry sector, supported by subsidies and technological advancements, can significantly boost productivity and contribute to overall economic development. Addressing rapid population growth through policies centered on education, healthcare, and employment will further support sustainable long-term economic growth. Future research should delve deeper into sector-specific analyses, especially of the agriculture and forestry sectors, to explore their broader impact on economic performance. Moreover, investigating the roles of political stability and governance, as well as the implications of foreign and domestic debt, will provide a more nuanced understanding of Pakistan’s economic landscape. Employing advanced econometric models and conducting longitudinal studies will enhance the analysis of dynamic interactions among inflation, GDP growth, trade, and investment, offering a more comprehensive view of macroeconomic policy effects over time. In this way, future research can contribute to a more holistic understanding of Pakistan’s economic challenges and opportunities, ultimately guiding the economy toward sustainable economic growth.