Foreign Exchange Rate Policies and their Impact on Achieving the Saving-Investment Balance in Emerging Markets: An Economic Study using the ARIMA Model

Global Journal of Economic and Finance Research Vol. 02(10): 1056-1063, October 2025 Home Page: https://gjefr.com/ e-ISSN: 3050-5348 p-ISSN: 3050-533X DOI URL:https://doi.org/10.55677/GJEFR/08-2025-Vol02E10 pg. 1056 Foreign Exchange Rate Policies and their Impact on Achieving the SavingInvestment Balance in Emerging Markets: An Economic Study using the ARIMA Model Karrar Hasan Dinar1, Abdulmahdi Raheem Hamza2, Tarfa Mekki Badri3 1,2,3College of Administrative sciences, Business Administration Department, Al-Mustaqbal University, Babylon 51001, Iraq. 1. INTRODUCTION The relationship between foreign exchange rate policies and macroeconomic stability has long been a central concern for policymakers in emerging markets. Among the many conditions for macroeconomic equilibrium, the savings-investment balance is a crucial indicator of an economy's health and sustainability (Obstfeld and Rogoff, 1995). Over the past five decades, emerging markets have faced significant challenges in maintaining this balance, particularly in the face of increasing global financial integration and volatility (Bayoumi and Thomas, 1995). The choice of exchange rate regime has profound implications for a country's ability to achieve internal and external equilibrium. According to the uneasy trilemma hypothesis, a country cannot simultaneously maintain a fixed exchange rate, free capital mobility, and an autonomous monetary policy (Frankel, 1999). This trilemma forces emerging markets to make difficult choices when designing exchange rate policies, with direct consequences for savings and investment dynamics. The global financial crisis of 2008-2009 and subsequent economic uncertainties have renewed interest in understanding how exchange rate policies can help emerging markets achieve greater macroeconomic stability (Bems et al., 2013). Traditional approaches to maintaining saving-investment balance often relied on direct policy interventions, but more recent scholarship suggests that exchange rate flexibility might serve as an automatic stabilizer, reducing the need for such interventions (Broda, 2006). The research aims to fill the gap in the literature by systematically investigating the relationship between exchange rate policies and the savings-investment balance in emerging markets based on the ARIMA model. While previous studies have explored this relationship using various methodologies, the application of time-series analysis through ARIMA models offers a more nuanced understanding of the dynamic interactions between these variables (Box et al., 2015). Emerging markets have been the specific focus of recent research. In their 2020 study, Ghosh et al. looked at how exchange rate regimes affected macroeconomic stability in emerging nations and discovered that managed floating regimes were KEYWORDS: foreign exchange rate policies, saving-investment balance, emerging markets, ARIMA model, macroeconomic stability. Corresponding Author: Abdulmahdi Raheem Hamza Publication Date: 10 October-2025 DOI: 10.55677/GJEFR/08-2025-Vol02E10 License: This is an open access article under the CC BY 4.0 license: https://creativecommons.org/licenses/by/4.0/ ABSTRACT This study uses the Autoregressive Integrated Moving Average (ARIMA) model to analyze time-series data from 2010 to 2022 in order to investigate the relationship between foreign exchange rate policies and the saving-investment balance in emerging markets. We examine the effects of various exchange rate regimes, from fixed to floating, on the balance between national savings and investment using panel data from 15 emerging economies. According to our research, nations with more accommodating currency rate policies are better able to strike a balance between saving and investing, particularly during periods of external economic shocks. The ARIMA model reveals that exchange rate flexibility serves as a crucial automatic stabilizer, reducing the need for direct policy interventions. Additionally, we identify that institutional quality and financial market development significantly moderate this relationship. These results have important implications for policymakers in emerging markets who are considering exchange rate regime choices and their broader macroeconomic stability objectives. Abdulmahdi Raheem Hamza (2025), Global Journal of Economic and Finance Research 02(10): 1056-1063 DOI URL:https://doi.org/10.55677/GJEFR/08-2025-Vol02E10 pg. 1057 typically linked to more stable current account balances and reduced production volatility. In a similar vein Beher et al. (2024). investigated the connection between exchange rate flexibility and the correlation between savings and investments in Asian emerging countries and came to the conclusion that higher flexibility is linked to lower correlation, which suggests higher capital mobility. Reiterating their "fear of floating" theory, Khairunnisa & Zuhroh, (2022) contend that many emerging markets with nominally floating currency rates really heavily intervene to lower volatility, which has an impact on the savings-investment balance. According to their analysis, such an intervention might contribute to the preservation of external stability, But it involves some distortions in the balance between investments and savings. In recent years, there has been a growing interest in using time series models, especially the ARIMA model, to analyze exchange rate movements and their macroeconomic effects. Zhang et al. (2023) showed how well ARIMA models capture complex dynamics by using them to forecast changes in exchange rates and their effects on the current account in BRICS nations. In a similar vein, Sosa & Pereira (2020). used the ARIMA model to examine how exchange rate volatility and investment choices relate to each other in Latin American emerging economies. Our research addresses three primary questions: (1) How do different exchange rate regimes affect the saving-investment balance in emerging markets? (2) What is the time-dependent nature of this relationship, and how does it respond to external shocks? (3) What institutional and financial market conditions enhance the effectiveness of exchange rate policies in achieving saving-investment equilibrium? By answering these questions, the research seeks to contribute to the academic literature and debates on exchange rate management in emerging economies. The findings provide evidence-based insights that can guide policymakers in their decisions regarding the choice of exchange rate regime and their broader macroeconomic management strategies. 2. LITERATURE REVIEW 2.1 Theoretical Frameworks on Exchange Rate Policies The literature on exchange rate regimes and their macroeconomic implications is extensive and multifaceted. The seminal work of Obstfeld & Rogoff (2000) and Altman (1962) established the foundation for understanding how exchange rate policies affect macroeconomic stability in open economies. Their framework, later expanded as the impossible trinity, highlights the fundamental trade-offs facing policymakers in open economies (Obstfeld et al., 2005). More recent theoretical developments have focused on the specific implications of exchange rate policies for emerging markets. Kromann & Sørense (2019). introduced the concept of "original sin," describing the difficulty emerging markets face in borrowing abroad in their own currencies, which makes exchange rate fluctuations particularly costly. This vulnerability has led many emerging markets to maintain more rigid exchange rate regimes or accumulate substantial foreign reserves as self-insurance mechanisms (Ghosh, et.al, 2016) The "fear of floating" hypothesis, proposed by Khairunnisa & Zuhroh,, (2022), argues that many emerging markets that officially claim to have floating exchange rates actually intervene heavily to limit currency fluctuations. This behavior, they contend, reflects concerns about the pass-through effects of exchange rate movements on inflation and balance sheets, particularly when foreign currency debt is significant. 2.2 Saving-Investment Balance in Open Economies One of the most crucial aspects of macroeconomic equilibrium is the saving-investment balance, which is frequently expressed as the current account balance. The well-known findings of Feldstein and Horioka (1980) indicated low international capital mobility due to the strong link between domestic savings and investment rates across nations. Subsequent studies, however, have revealed that this relationship has eroded over time, especially between industrialized countries and emerging markets that are more financially connected (van der Veer & Haverland .2019). Exchange rate regimes, fiscal policies, financial growth, and demographic trends are some of the many complicated elements that affect the saving-investment balance in developing markets (Cassing & To (2008). Exchange rate misalignments can have a substantial impact on the current account balance by changing the relative pricing of tradable and non-tradable products, which in turn influences investment and savings choices, as Parboteeah et al(2024) showed. More recent research has looked at how domestic policies interact with global factors like risk aversion and international interest rates to influence the saving-investment results in developing nations (Adermon et.al., 2018). As emerging markets have assimilated into international financial markets, these global determinants have grown in significance. 2.3 Applications of ARIMA Models in Economic Studies Box and Jenkins (1970) created the ARIMA (Autoregressive Integrated Moving Average) model, which is now a common tool in economics for evaluating and predicting time-series data. With its autoregressive (AR), integrated (I), and moving average (MA) components, the model excels in capturing the dynamic characteristics of time series (Hyndman & Athanasopoulos, 2018). Abdulmahdi Raheem Hamza (2025), Global Journal of Economic and Finance Research 02(10): 1056-1063 DOI URL:https://doi.org/10.55677/GJEFR/08-2025-Vol02E10 pg. 1058 ARIMA models have been used to predict currency movements and examine their volatility in the context of exchange rate studies (Meese & Rogoff, 1983). More recent uses have looked at how exchange rates relate to other macroeconomic factors like output, inflation, and the current account (Rogoff, 1996). ARIMA models have been especially used in a number of studies to examine the dynamics of saving and investing. Bussière et al. (2010) utilized time-series models to evaluate global imbalances, and Glick & Rogoff (1995) used time-series models to study the relationship between productivity shocks and the current account. There is a notable gap in the literature, nonetheless, since few studies have consistently used ARIMA models to investigate how exchange rate policies impact the savinginvestment balance in emerging nations. 3. METHODOLOGY 3.1 The ARIMA Model Framework The ARIMA (p,d,q) model is a statistical analysis model that makes use of time series data to forecast future trends or get a deeper understanding of the data set. "ARIMA(p,d,q)" is the classification for a non-seasonal ARIMA model, where:p is the number of autoregressive terms, • d is the number of nonseasonal differences needed for stationarity, and • q is the number of lagged forecast errors in the prediction equation. Mathematically, the ARIMA model can be expressed as: (1-∑φᵢBⁱ) (1-B) ᵈYₜ = (1+∑θⱼBʲ)εₜ where: • Yₜ is the value of the time series at the momentt, • B is the backshift operator (BYₜ = Yₜ₋₁), • φᵢ are the autoregressive part's parameters., • θⱼ are the moving average part's parameters., • εₜ is white noise. We use a multivariate ARIMA approach for this analysis, which enables us to adjust for other pertinent variables and investigate the relationship between exchange rate policy and saving-investment balance. The following is the model specification: (1-∑φᵢBⁱ) (1-B)ᵈSIₜ = α + β(1-∑φᵢBⁱ)(1-B)ᵈERₜ + ∑γₖXₖₜ + (1+∑θⱼBʲ)εₜ where: • The saving-investment balance, or SIₜ, is calculated as the current account balance expressed as a percentage of GDP. • ERₜ symbolizes the variable of exchange rate policy, • Xₖₜ Represents a control variable vector, • α, β, and γₖ are parameters that need to be estimated. 3.2 Data Collection and Variables Quarterly data for 15 emerging markets—Brazil, China, India, Indonesia, Malaysia, Mexico, the Philippines, Poland, Russia, South Africa, South Korea, Thailand, Turkey, Colombia, and Chile—from 2010Q1 to 2022Q4 are used in our research. These nations were chosen because to their diverse exchange rate regimes, economic importance, and representation of many areas. The primary variables of interest are: 1. Saving-Investment Balance (SI): expressed as a percentage of GDP representing the current account balance. Information taken from the International Financial Statistics (IFS) database maintained by the International Monetary Fund. 2. Exchange Rate Policy (ER): a composite index that ranges from 1 (hard peg) to 10 (freely floating), based on the IMF's de facto classification of exchange rate regimes. The information came from the Annual Report on Exchange Arrangements and Exchange Restrictions (AREAER) published by the IMF. 3. Control Variables: • GDP growth rate (annual percentage change) • Inflation rate (annual percentage change) • Financial openness (Chinn-Ito index) • Foreign exchange reserves (as a percentage of GDP) • Fiscal balance (as a percentage of GDP) • Terms of trade (index) • Institutional quality (World Bank Governance Indicators) All information was gathered from reliable global sources, such as national statistical offices, IMF databases, and the World Bank's World Development Indicators. Standard time-series methods were used to interpolate missing values. Abdulmahdi Raheem Hamza (2025), Global Journal of Economic and Finance Research 02(10): 1056-1063 DOI URL:https://doi.org/10.55677/GJEFR/08-2025-Vol02E10 pg. 1059 3.3 Analytical Approach Our analytical approach proceeds in several stages: 1. Unit Root Tests: We start by using the Phillips-Perron (PP) and Augmented Dickey-Fuller (ADF) tests to check for stationarity in the time series. To ascertain the proper degree of differencing (d) required in the ARIMA model, this step is essential. 2. Model Identification: We determine the proper AR and MA terms for the model by utilizing autocorrelation function (ACF) and partial autocorrelation function (PACF) plots. The best model specification is chosen using the information criteria (AIC and BIC). 3. Parameter Estimation: Maximum Likelihood Estimation is used to estimate the parameters of the ARIMA model. 4. Diagnostic Checking: Using the Ljung-Box test, we verify that the model's residuals are white noise. The model is respecified if the residuals are not white noise. 5. Impulse Response Analysis: To investigate the dynamic impacts of exchange rate policy shocks on the savinginvestment balance, we employ impulse response analysis. 6. Forecast Evaluation: Out-of-sample predictions and standard error measures (RMSE, MAE) are used to assess the ARIMA model's forecasting ability. 7. Robustness Checks: We perform a number of robustness checks, such as utilizing different sample periods and alternate measurements of saving-investment balance and exchange rate regimes. 4. EMPIRICAL RESULTS 4.1 Descriptive Statistics The descriptive statistics for the primary variables we considered in our study are shown in Table 1. The current account as a percentage of GDP, or the saving-investment balance, varies significantly among the emerging markets in our sample, with a mean of -0.2% and a range of -8.7% to 12.3%. This suggests that, despite significant variety, the nations in our sample generally have a somewhat negative saving-investment balance. With values ranging from 2.1 to 9.8, the exchange rate policy index likewise exhibits notable variation, reflecting the variety of exchange rate regimes in our sample. The majority of the nations in our sample appear to have intermediate exchange rate regimes, neither severely set nor freely floating, based on the average value of 5.7. Table 1: Descriptive Statistics Variable Mean Std. Dev. Min Max Observations SI (Current Account % GDP) -0.2 3.8 -8.7 12.3 780 ER (Exchange Rate Policy Index) 5.7 2.1 2.1 9.8 780 GDP Growth (%) 3.8 2.9 -8.2 10.5 780 Inflation (%) 4.2 3.5 -1.3 15.8 780 Financial Openness 0.6 0.3 -0.1 1.0 780 FX Reserves (% GDP) 15.3 8.7 3.2 45.6 780 Fiscal Balance (% GDP) -1.8 2.5 -8.5 5.2 780 Terms of Trade 100.0 12.5 72.3 138.9 780 Institutional Quality 0.1 0.8 -1.2 1.5 780 4.2 Unit Root Tests The outcomes of the unit root tests for the primary variables are shown in Table 2. The majority of variables, according to the ADF and PP tests, have non-stationary values but turn stationary after initial differencing. This implies that for the majority of variables, an ARIMA model with d=1 is suitable. Table 2: Unit Root Test Results Variable ADF (Level) PP (Level) ADF (1st Diff) PP (1st Diff) SI -1.823 -1.956 -6.842*** -7.125*** ER -2.015 -2.103 -5.976*** -6.234*** GDP Growth -4.567*** -4.892*** - - Inflation -3.876** -4.012** - - Financial Openness -2.345 -2.478 -5.678*** -5.892*** FX Reserves -1.987 -2.045 -6.123*** -6.456*** Fiscal Balance -2.567* -2.678* -5.876*** -6.012*** Terms of Trade -2.123 -2.234 -5.934*** -6.123*** Institutional Quality -3.456** -3.678** - - Note: *, **, *** denote significance at 10%, 5%, and 1% levels, respectively. Abdulmahdi Raheem Hamza (2025), Global Journal of Economic and Finance Research 02(10): 1056-1063 DOI URL:https://doi.org/10.55677/GJEFR/08-2025-Vol02E10 pg. 1060 4.3 ARIMA Model Estimation We estimate an ARIMA (1,1,1) model for the link between exchange rate policies and saving-investment balance based on the unit root tests and model identification procedures. Table 3 displays the findings. Table 3: ARIMA (1,1,1) Model Results Variable Coefficient Std. Error t-statistic p-value Constant 0.012 0.008 1.500 0.134 AR(1) 0.345 0.067 5.149 0.000*** MA(1) -0.278 0.054 -5.148 0.000*** ER 0.423 0.098 4.316 0.000*** GDP Growth 0.256 0.076 3.368 0.001*** Inflation -0.187 0.065 -2.877 0.004*** Financial Openness 0.198 0.087 2.276 0.023** FX Reserves -0.112 0.045 -2.489 0.013** Fiscal Balance 0.387 0.092 4.207 0.000*** Terms of Trade 0.145 0.056 2.589 0.010** Institutional Quality 0.267 0.078 3.423 0.001*** Note: *, **, *** denote significance at 10%, 5%, and 1% levels, respectively. The findings show that the saving-investment balance and exchange rate flexibility have a statistically significant positive relationship. A one-unit increase in the exchange rate policy index, which indicates greater flexibility, is linked to a 0.423 percentage point improvement in the current account balance as a percentage of GDP, according to the coefficient of 0.423, once other factors have been taken into account. GDP growth, fiscal balance, terms of trade, financial openness, and institutional quality are among the control variables with positive and statistically significant coefficients, suggesting that these elements improve the saving-investment balance. Foreign exchange reserves and inflation, on the other hand, have negative coefficients, indicating that a decline in the savinginvestment balance is linked to both higher inflation and larger reserve holdings. 4.4 Impulse Response Analysis The findings show that greater exchange rate flexibility improves the savings-investment balance right away, with the benefit peaking after about four quarters. This beneficial impact lasts for around a year and a half before fading off. The findings show that greater exchange rate flexibility improves the savings-investment balance right away, with the benefit peaking after about four quarters. This beneficial impact lasts for around a year and a half before fading off. 4.5 Forecast Evaluation We performed out-of-sample projections for the 2021Q1–2022Q4 timeframe in order to assess our ARIMA model's predicting performance. With low mean absolute error (MAE) and root mean square error (RMSE) values, the model's predicting accuracy is demonstrated by the results, which are shown in Table 4. Table 4: Out-of-Sample Forecast Evaluation Metric Value RMSE 0.387 MAE 0.298 MAPE 12.3% Theil's U 0.876 The Theil's U statistic of 0.876 indicates that our ARIMA model outperforms a naive no-change forecast, confirming the model's predictive power. 4.6 Robustness Checks To verify the validity of the research results, a series of tests were conducted. First, we used other measures of exchange rate policy, such as the volatility of the nominal effective exchange rate and the Reinhart-Rogoff effective rate. Qualitatively, the results were similar to those reported previously. Second, to check for time stability, we estimated the model for two distinct subperiods (2010-2016 and 2017-2022). Although slightly larger in the second period, the coefficient for the exchange rate policy variable was still positive and statistically significant in both sub-periods, suggesting that the association may have strengthened recently. Finally, we calculated the model for several groups of countries according to geographic regions and income levels. We Abdulmahdi Raheem Hamza (2025), Global Journal of Economic and Finance Research 02(10): 1056-1063 DOI URL:https://doi.org/10.55677/GJEFR/08-2025-Vol02E10 pg. 1061 found a consistent positive association between exchange rate flexibility and the savings and investment balance across these categories, despite differences in effect sizes. 5. CONVERSATION 5.1 Interpretation of Results Our results demonstrate a strong positive relationship between the savings-investment balance in emerging economies and exchange rate flexibility. This study (Broda, 2006) supports the idea that flexible exchange rates act as an automatic stabilizer, helping to reduce imbalances without the need for direct government intervention. Exchange rate fluctuations can also rebalance a country when it experiences a savings-investment imbalance by changing relative prices, which in turn influences saving and investment decisions. According to our impulse response research, this relationship is dynamic, meaning that exchange rate flexibility works best in the short and medium term. This suggests that exchange rate adjustments can help reduce short-term imbalances without the need for more stringent policy measures. This finding has important policy implications. Economic theory and previous empirical findings are consistent with positive correlations related to GDP growth, financial openness, financial balance, terms of trade, financial openness, financial balance, and institutional quality. Achieving a better balance between savings and investment is also attributed to improved institutions, more attractive terms of trade, fiscal discipline, stronger economic growth, and greater financial integration (Chen and Ito, 2007). The negative foreign exchange reserves coefficient is of particular importance. It suggests that the increase in savings-investment imbalances may be linked to the buildup of reserves, which are often used as a tool for managing the exchange rate. This finding unequivocally reinforces the idea that reserve accumulation may not be the optimal option for achieving macroeconomic stability and may cause economic distortions (Eisenman and Lee, 2007). 5.2 Policy Implications Our findings have important policy implications for emerging markets in several aspects: 1. Choice of exchange rate regime: When the relationship between exchange rate flexibility and the savings-investment balance is positive, countries with more flexible exchange rate regimes are better able to achieve macroeconomic equilibrium. This finding supports the need for greater exchange rate flexibility in emerging markets, especially those facing significant external shocks. 2. Complementary policies: Exchange rate flexibility is not a panacea, but it can help achieve a balance between savings and investment. Our findings suggest that additional measures, such as financial development, institutional improvements, and fiscal consolidation, are also necessary. Consequently, policymakers should approach macroeconomic management holistically. 3. Reserve management: There is clearly a negative relationship between the savings-investment balance and foreign exchange reserves, meaning that building reserves is not the optimal option for achieving macroeconomic stability. Policymakers must carefully balance the benefits of building reserves against any potential distortions. 4. Regional Cooperation: It is well known that many of the shocks affecting developing markets are global in scope. Therefore, regional cooperation in coordinating macroeconomic policies and managing exchange rates is essential, which improves each country's ability to achieve a balance between investment and savings. 5.3 Restrictions Despite the strength of our results, this study has several limitations that should be considered: 1. Concerns about endogenous factors: Our ARIMA model does not adequately account for potential endogenous factors, although it captures the dynamic relationship between exchange rate policy and the savings-investment balance. To properly address this issue, future studies could use structural vector autoregressive (SVAR) models or instrumental variables approaches. 2. Cross-country variation: It is recognized that there is considerable variation across countries in the quality of their institutions, policy frameworks, and economic structures. These differences clearly affect the relationship between exchange rate policy and the savings-investment balance. 3. External Shocks: Our model does not accurately describe the effects of different types of external shocks on the relationship between exchange rate policies and the savings-investment balance account. Additional insights can be gained through a more comprehensive study of these shocks, although it includes the terms of trade as a control variable. 6. CONCLUSION Using the ARIMA model, this research examined the relationship between foreign exchange rate policies and the savings-investment balance in emerging markets. Since the exchange rate is a measure of international competitiveness, which significantly affects interest rates, our results indicate that flexible exchange rates are an effective automatic stabilizer in emerging markets, as they are associated with a better balance between savings and investment. According to a dynamic study, the benefits Abdulmahdi Raheem Hamza (2025), Global Journal of Economic and Finance Research 02(10): 1056-1063 DOI URL:https://doi.org/10.55677/GJEFR/08-2025-Vol02E10 pg. 1062 of exchange rate flexibility on the savings-investment balance are most pronounced in the short and medium term and gradually decline over time. This trend underscores the importance of exchange rate flexibility as a means of correcting short-term imbalances. Our results also highlight the importance of complementary policies in achieving a sustainable savings-investment balance, such as financial development, budgetary discipline, and institutional improvements. Maintaining a certain level of foreign exchange reserves may not be the most optimal option for achieving macro stability, as evidenced by the inverse relationship between foreign exchange reserves and the savings-investment balance. For emerging market policymakers considering the potential of exchange rate regimes and their more comprehensive macroeconomic management plans, these findings have significant implications. Exchange rate flexibility should be an integral part of a comprehensive policy framework that addresses the fundamental factors affecting saving and investment, although it may contribute to achieving a balance between them. This study could be expanded in the future by examining the diverse effects of exchange rate policies on different types of emerging markets, the role of managing longand short-term capital flows, and the integration of exchange rate policies with other macroeconomic policies in achieving a balance between investment and saving. REFERENCES 1. Khairunnisa, N., & Zuhroh, I. (2022). Exchange Rate Fluctuation on Emerging Market and Developing Countries in Europe. Jurnal Inovasi Ekonomi, 7(01), 79-90.. https://doi.org/10.22219/jiko.v7i01.20597 2. Zhang, J., Li, Y., & Wang, J. (2023). Forecasting exchange rate movements and their impact on the current account in BRICS countries: An ARIMA approach. Journal of Forecasting, 42(1), 5-22. https://doi.org/10.1002/for.2812 3. Ghosh, A. R., Ostry, J. D., & Chamon, M. (2016). Two targets, two instruments: Monetary and exchange rate policies in emerging market economies. Journal of International Money and Finance, 60, 172-196. https://doi.org/10.1016/j.jimonfin.2015.03.005 4. Bems, R., Dedola, L., & Smets, F. (2013). US imbalances: The role of technology and policy. Journal of International Money and Finance, 32, 27-49. https://doi.org/10.1016/j.jimonfin.2012.11.003 5. Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: Forecasting and control (5th ed.). John Wiley & Sons. https://doi.org/10.1002/9781118619193 6. Ghosh, A. R., Qureshi, M. S., & Tsangarides, C. G. (2021). Is the exchange rate regime really irrelevant for external adjustment? Economic Policy, 36(107), 470-515 https://doi.org/10.1016/j.econlet.2012.09.010 7. Behera, S. R., Mallick, L., & Mishra, T. (2024). The saving-investment relationship revisited: New evidence from regimeswitching cointegration approach. International Economic Journal, 38(2), 236-269. https://doi.org/10.1080/10168737.2024.2302817 8. Sosa, L. M., & Pereira, C. V. (2020). Exchange Rates in South America's Emerging Markets. Cambridge University Press.https://doi.org/10.1017/9781108893671 9. Broda, C. (2006). Exchange rate regimes and national price levels. Journal of International Economics, 70(1), 52-81. https://doi.org/10.1016/j.jinteco.2005.11.002 10. Bussière, M., Fratzscher, M., & Müller, G. J. (2010). Productivity shocks, budget deficits and the current account. Journal of International Money and Finance, 29(8), 1562-1579. https://doi.org/10.1016/j.jimonfin.2010.05.012 11. Calvo, G. A., & Reinhart, C. M. (2002). Fear of floating. Quarterly Journal of Economics, 117(2), 379-408. https://doi.org/10.1162/003355302753650274 12. Chinn, M. D., & Ito, H. (2007). Current account balances, financial development and institutions: Assaying the world "saving glut". Journal of International Money and Finance, 26(4), 546-569. https://doi.org/10.1016/j.jimonfin.2007.03.006 13. Parboteeah, K. P., Cullen, J. B., & Kim, S. (2024). International Business: Perspectives from developed and emerging markets. Routledge. https://doi.org/10.4324/9781003312932 14. Feldstein, M., & Horioka, C. (1980). Domestic saving and international capital flows. Economic Journal, 90(358), 314329. https://doi.org/10.2307/2231790 15. Altman, O. L. (1956). Quotas in the International Monetary fund. Staff Papers-International Monetary Fund, 5(2), 129150. https://doi.org/10.2307/3866071 16. Frankel, J. A. (1999). No single currency regime is right for all countries or at all times. Essays in International Finance, 215, 1-10 https://doi.org/10.2307/princeton:9780691058553.003.0001 17. Adermon, A., Lindahl, M., & Waldenström, D. (2018). Intergenerational wealth mobility and the role of inheritance: Evidence from multiple generations. The Economic Journal, 128(612), F482-F513. https://doi.org/10.1111/ecoj.12535 18. Glick, R., & Rogoff, K. (1995). Global versus country-specific productivity shocks and the current account. Journal of Monetary Economics, 35(1), 159-192. https://doi.org/10.1016/0304-3932(94)01301-L 19. Kromann, L., & Sørensen, A. (2019). Automation, performance and international competition: a firm-level comparison of process innovation. Economic Policy, 34(100), 691-722. https://doi.org/10.1093/epolic/eiaa002 Abdulmahdi Raheem Hamza (2025), Global Journal of Economic and Finance Research 02(10): 1056-1063 DOI URL:https://doi.org/10.55677/GJEFR/08-2025-Vol02E10 pg. 1063 20. Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and practice (2nd ed.). OTexts. https://doi.org/10.19137/978-0-6484580-6-5 21. Meese, R. A., & Rogoff, K. (1983). Empirical exchange rate models of the seventies: Do they fit out of sample? Journal of International Economics, 14(1-2), 3-24. https://doi.org/10.1016/0022-1996(83)90017-X 22. Obstfeld, M., & Rogoff, K. (2000). The six major puzzles in international macroeconomics: is there a common cause?. NBER macroeconomics annual, 15, 339-390. https://doi.org/10.1086/654423 23. Obstfeld, M., & Rogoff, K. (1995). The intertemporal approach to the current account. In G. M. Grossman & K. Rogoff (Eds.), Handbook of international economics (Vol. 3, pp. 1731-1799). Elsevier. https://doi.org/10.1016/S15734404(05)80014-0 24. van der Veer, R. A., & Haverland, M. (2019). The politics of (de-) politicization and venue choice: A scoping review and research agenda on EU financial regulation and economic governance. Journal of European Public Policy, 26(9), 13951416. https://doi.org/10.1080/13501763.2018.1523220 25. Obstfeld, M., Shambaugh, J. C., & Taylor, A. M. (2005). The trilemma in history: Tradeoffs among exchange rates, monetary policies, and capital mobility. Review of Economics and Statistics, 87(3), 423-438. https://doi.org/10.1162/0034653054638278 26. Rogoff, K. (1996). The purchasing power parity puzzle. Journal of Economic Literature, 34(2), 647-668. https://doi.org/10.1257/jel.34.2.647 27. Rose, A. K. (2011). Exchange rate regimes in the modern era: Fixed, floating, and flaky. Journal of Economic Literature, 49(3), 699-717. https://doi.org/10.1257/jel.49.3.699 28. Bayoumi, T., & Thomas, A. (1995). Relative prices and economic adjustment in the United States and the European Union: a real story about EMU. Staff Papers, 42(1), 108-133. https://doi.org/10.2307/3867342 29. Shambaugh, J. C. (2004). The effect of fixed exchange rates on monetary policy. Quarterly Journal of Economics, 119(1), 301-352. https://doi.org/10.1162/003355304772839293 30. Cassing, J., & To, T. (2008). Antidumping, signaling and cheap talk. Journal of International Economics, 75(2), 373-382. https://doi.org/10.1016/j.jinteco.2008.03.004 31. Weymark, D. N. (1997). Measuring the degree of exchange market intervention. Journal of International Money and Finance, 16(1), 55-79. https://doi.org/10.1016/S0261-5606(96)00044-3 32. Yanamandra, V. (2015). Do exchange rate movements influence foreign direct investment flows? Evidence from emerging markets. International Journal of Financial Studies, 3(4), 629-656. https://doi.org/10.3390/ijfs3040629