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This study uses a quantitative associative approach to analyze the influence of exports, imports, inflation, and exchange rates on economic growth in five ASEAN member countries: Indonesia, Malaysia, Singapore, Thailand, and Vietnam. The data used are secondary data obtained from the World Bank for the period 2013–2023. The analysis technique used is the Panel Autoregressive Distributed Lag (Panel ARDL) Model, which begins with stationarity and cointegration tests. Results The ARDL Panel Model estimation in this study is declared valid because it meets the main requirements, namely having a cointegrated lag with a negative coefficient value of -0.831550 and significant at the 5% significance level (probability 0.0000 < 0.05). The long-term estimation results indicate that only the inflation variable has a significant influence on Gross Domestic Product (GDP) in the 5 ASEAN countries studied. Meanwhile, in the short term, no variables were found to have a significant influence on GDP in the 5 countries. Furthermore, country-level estimations show varying results. Indonesia is the only country that shows a significant influence of exports, imports, inflation, and exchange rates on GDP. Thailand shows a significant influence of exports and exchange rates, while Malaysia, Singapore, and Vietnam do not show any significant influence of exports, imports, inflation, and exchange rates on GDP. These findings reflect that the relationship between macroeconomic variables and economic growth in ASEAN countries is heterogeneous and is strongly influenced by the structural characteristics of each country.

Researchers in this study aim to determine the Natural Resources-Based Green Trade Model for Green Growth in Brazil, Russia, Indonesia, Singapore, India and China (BRISIC). The method in this research is using the ARDL Panel method. The variables used in this research are Green Growth, Green Trade, Natural Resources, Financial Inclusion, Green Innovation, Digital Economy. The results of this research are from the Green Trade Model Based on Natural Resources on Green Growth in Brazil, Russia, Indonesia, Singapore, India and China (BRISIC) using the panel method, it can be concluded that in BRISIC countries the variable that has an overall influence is green trade. and Natural resources both Short Run Equation and Long Run Equation.

Using a quantitative approach, this study investigates the effect of port export and import volumes on economic growth in North Sumatra and West Sumatra Provinces. Time series data from the World Bank and the Central Statistics Agency (BPS) from 2006 to 2023 are used as secondary data. The analysis uses the ARDL Panel model, which allows for analysis of data dynamics across time and regions. The results show that the three main indicators that affect economic growth (GRDP) in both provinces, both in the short and long term, are export volume, inflation, and exchange rates. In North Sumatra, export volume has a positive impact on GRDP, while import volume has a negative impact, indicating a risk of dependence on imports. Controlled inflation also has a positive impact, while the exchange rate shows a diversion. Policy recommendations are expected to improve global competitiveness and exchange rate stability through coordination of fiscal and monetary policies, support for the Export Capacity Building Program and MSMEs through the Regional Comprehensive Economic Framework (RCEP), and export diversification to reduce dependence on certain commodities. This study emphasizes that policies that are responsive to changes in trade at the national to international levels are an important foundation for stabilizing sustainable economic growth.

This research to analyze the contribution of variables from three economic policies, with monetary policy through interest rate variables, exchange rates, and money supply in facing economic recession. Where the fiscal policy variable is through tax value. Then macroprudential policy through Non Performing Loan and Capital Adequacy Ratio variables. This study uses secondary data or time series, namely from December 2019 to February 2021. The data analysis model in this study is the Vector Autoregression (VAR) model which is seen from being sharpened with Impulse Response Function (IRF) analysis and Forecast Error Variance Decomposition (FEVD), Panel ARDL, and Different Tests. The results of the IRF analysis show that the stability of the response of all variables is formed in period 8 or the medium and long term, where the response of other variables to changes in one variable shows different variations, both from positive responses to negative responses or vice versa, and there are variables whose responses remain positive or remain negative from the short term to the long term. The results of the FEVD analysis show that for the short-term inflation variable it is influenced by inflation itself and in the medium and long term it is influenced by interest rates. For the JUB variable in the short term it is influenced by JUB itself and in the medium and long term it is influenced by NPL. For the interest rate variable in the short term it is influenced by JUB while in the medium and long term it is influenced by the exchange rate itself and CAR. For the tax variable in the short, medium and long term it is influenced by the tax itself and JUB. For the NPL variable in the short, medium and long term it is influenced by JUB and tax. For the CAR variable in the short, medium and long term it is influenced by JUB and tax. Then the results of the ARDL Panel analysis show that the country that is able to become a leading indicator in controlling the economic recession in the Four of The Group Twenty, namely Turkey, is only done by interest rates. While South Africa is done by interest rates, taxes, NPL, and CAR. For Russia, it is done by all variables, namely the amount of money in circulation, interest rates, exchange rates, taxes, NPL, and CAR. Meanwhile, Indonesia is carried out by exchange rates, taxes, NPL and CAR.

The financial system plays an important role in the economy. An unstable financial system will be vulnerable to various problems that disrupt the rotation of a country's economy and be vulnerable to economic problems such as the global crisis in various countries. The problem that occurs is the occurrence of Covid-19 causing various fluctuations in the level of inflation, money supply, imports, the occurrence of unstable inflation from January 2019 to August 2021, low inflation resulting in a decrease in imports and an increase in the money supply in Mexico. , Vietnam, Philippines, Hongkong, Indonesia, Canada, Malaysia, Singapore, Peru, and China. The analytical method in this study uses the ARDL Panel (Autoregression Distributed Lag) approach. The ARDL Panel Model determines which country models from APEC countries are able to control long-term financial system-based economic fundamentals in Mexico, Vietnam, the Philippines, Hong Kong, Indonesia, Canada, Malaysia, Singapore, Peru, and China and the Different Test for modeling the impact of covid-19 19 on the economic fundamentals of the financial system. The results of the research found the ARDL Panel prediction model in modeling the impact of Covid-19 on economic fundamentals in the financial system. The main Leading Indicator of variable effectiveness in controlling Inflation In TAPEC is JUB where Vietnam, the Philippines, Hong Kong, Japan, Malaysia, Singapore, Peru and China have a significant influence in controlling Inflation. Then overall in the long term (Long Run) it turns out that only the JUB and CDV variables have an effect on INF In TAPEC, while in the short term (Short Run) it is JUB that influences Inflation In TAPEC.  

Destination from study this that is for test variable Interest Rates, Inflation , Total Money Supply and GDP how much big in take effect to EXCHANGE variable . And for knowing is panel level _ ethnic group interest , inflation , money supply , unemployment , investment , and GDP have an effect positive and significant to exchange rates in America, Australia, China, Canada , Indonesia, Japan , South Korea, Malaysia, Singapore, Russia and Thailand. Approach study this is study associative / quantitative with the Simultaneous model and the ARDL Panel where aim see linkages Among independent variables and dependent variables that spread panel in Top Major Exchange Rate countries in 11 APEC Countries. Study this conducted against 11 countries with exchange rate strongest in the APEC countries in the world (America, Australia, Malaysia, Singapore, South Korea, Japan , China, Indonesia, Canada , Russia , and Thailand). The ARDL Panel Analysis results show that the Leading Model Control indicators Exchange Rate System Through the Post -Covid-19 Open Economy Model, the Top Major Exchange Rates in Eleven Apec Countries (Varies) are JUB and GDP. this _ due to the results data processing , the ROE variable is variable that gives stable influence , ie _ effect on the inside period long nor period short in give influence significant to score exchange , which is assessed from level short run and long run stability in the table result .