A Dynamic Factor Approach to Forecasting the Index of Industrial Production of Sri Lanka

The Index of Industrial Production (IIP) is a leading indicator used by economic forecasters to predict the overall economic activity of a country. However, no published studies up to date have analyzed and forecasted the IIP of Sri Lanka, which is the only measure of the nation’s physical volume of production. On the other hand, the evolution of information technology has allowed researchers to access thousands of economic series with incredible ease. The usual time series models, such as the widely used Vector Autoregressive (VAR) model can only incorporate a limited number of variables due to the issue of scarce degrees of freedom. Limiting the VAR model to a selected number of variables results in the contamination of economic policy decisions. Dynamic Factor Models (DFMs) are a potential candidate to address this issue by incorporating all available information to the study. However, there is no literature on such applications on a small-scale economy such as Sri Lanka’s to forecast future trends. Therefore, this research aims to follow a dynamic factor approach to forecasting the IIP of Sri Lanka. The objectives of this study are twofold: to explore the different associations between the IIP and other economic indicators, and to investigate how well data-rich DFMs perform compared to classical time series and Long Short-Term Memory networks (LSTMs) when forecasting the IIP. The model estimation was based on the sample from 2000 January to 2018 December and finally, twelve month out-of-sample forecasts were produced for the period until 2019 December. Twenty-three monthly economic variables were considered. The results showed that DFMs, including Factor Augmented VAR (FAVAR) outperformed the traditional VAR, while LSTMs produced the best overall forecasts. Moreover, it was identified that investment imports, mineral and agricultural exports, Colombo Consumer Price Index (CCPI), electricity power consumption and gold price have a significant impact on the IIP.