Macroeconomic Forecasting using Dynamic Factor Models

During the recent period, dynamic factor modelling is gaining importance as one of the key forecasting tools exploiting the information contained in large datasets. The major advantage with the factor modelling approach is that, it can cope with many variables without running into scarce degrees of freedom that often arise in regression analysis. This technique allows forecasters to summarize the information contained in large datasets and extract a few common factors from them. This study attempts to develop a dynamic factor model (DFM) to forecast industrial production and price level in India. For this purpose, domestic as well as external economic indicators, that appear to contain information about the movement of industrial production/ price level, were used. Based on empirical analysis, it is found that the out-of-sample forecast accuracy of DFM, as measured by root mean square percentage error, is better than the OLS regression. JEL Classifi cation : C3, E2, E3.