Forecasting Inflation, Exchange Rate, and GDP using ANN and ARIMA Models: Evidence from Pakistan

Abstract

Purpose: The purpose of this study is to specify an efficient forecast model for the accurate prediction of macroeconomic variables in the context of Pakistan. Design/Methodology/Approach: We particularly investigate the comparative accuracy of Artificial Neural Network (ANN) and Autoregressive Integrated Moving Average (ARIMA) models-based predictions using monthly data of inflation, exchange rate, and GDP from 1990 to 2014. Findings: According to our findings, the ANN-based forecasted inflation series is more precise as compared to ARIMA-based estimates. On the contrary, the ARIMA model outperforms the ANN model for exchange rate forecasts with the forecasted values being very close to the actual values. Further, ARIMA performs comparatively better in forecasting GDP with relatively smaller forecast error. On the whole, our findings suggest the ARIMA model provides appropriate results for forecasting exchange rates and GDP, while the ANN model offers precise estimates of inflation. Implications/Originality/Value: Our findings have important implications for the analysts and policymakers highlighting the need to use appropriate forecasting models that are well aligned with the structure of an economy.