Modelling and Forecasting the USD-INR Exchange Rate Using MLR and ARIMA Approaches
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Abstract: Exchange rate fluctuations are a critical element in the economic performance of open economies, as they influence international trade, capital flows, investment planning, and policy development. For India, managing currency volatility is essential due to its increasing engagement in global markets. The Reserve Bank of India (RBI) frequently intervenes to regulate extreme movements in the exchange rate to safeguard macroeconomic stability. This study focuses on examining the USD-INR exchange rate in relation to four key macroeconomic variables: inflation rate, interest rate, unemployment rate, and GDP growth rate, considering both Indian and U.S. perspectives. To achieve this, two Multiple Linear Regression (MLR) models were constructed using annual data from 1991 to 2021, allowing for the assessment of each variable’s statistical influence and directional effect on the exchange rate. Alongside this, a time-series analysis was conducted using the ARIMA (Auto Regressive Integrated Moving Average) model to forecast monthly exchange rates, offering insights into future currency trends based on historical data patterns. The analysis revealed that macroeconomic indicators from the United States have a more substantial impact on the USD-INR exchange rate than those from India, underscoring the Indian rupee’s sensitivity to global economic conditions. The ARIMA (1,1,1) model emerged as the most suitable for forecasting purposes, providing reliable projections for the years 2021 and 2022.Overall, this research highlights the interconnected nature of global economies and emphasizes the importance of combining regression analysis with time-series forecasting to gain a comprehensive understanding of exchange rate behavior. The findings provide valuable input for policymakers, investors, and businesses engaged in international operations, as they navigate currency-related risks and develop informed strategies in a volatile global environment.
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