Macroeconomic forecasting for Pakistan in a data-rich environment

This article forecasts the CPI inflation, GDP growth and the weighted average overnight repurchase rate in Pakistan using 161 predictors covering a period from July 2007 to July 2017. We use the naïve mean model and the autoregressive model as benchmark models and compare their forecasting performance against the dynamic factor model (DFM) and sophisticated machine learning methods such as the Ridge regression, the LASSO, the Elastic net and a few variants of Bagging. The main purpose of the article is to determine, how well the commonly used DFM which has been used for time series forecasting for a long time, performs against the recently developed penalized regression methods in forecasting key macroeconomic variables in Pakistan. We forecast the variables of interest over 12 months forecast horizon. The forecast evaluation criteria used to compare the forecast performance of these models is the RMSE and MASE. For each variable of interest, we find that, for majority of the cases considered, one of the competing approaches outperform the benchmark models and other competing approaches at majority of forecast horizons. Our results show that, on the balance, the machine learning approaches perform better than the benchmark, the autoregressive and the DFM.