Forecasting GDP growth is essential for effective and timely implementation of macroeconomic policies. This paper uses a Principal Component augmented Time Varying Parameter Regression (TVPR) approach to forecast real aggregate and sectoral growth rates for India. We estimate the model using a mix of fiscal, monetary, trade and production side-specific variables. To assess the importance of different growth drivers, three variants of the model are used. In ‘Demand-side’ model, the set of variables exclude production-specific indicators, while in the ‘Supply-side’ model, information is extracted only from the latter set. The ‘Combined’ model consists of both sets of variables. We find that TVPR model consistently outperforms constant parameter factor-augmented regression model and Dynamic Factor Model in terms of forecasting performance for all the three specifications. Based on the TVPR model, we find that demand side variant minimises the error forecast for total GDP and the industrial sector GDP, while the supply side variant minimises the error forecast for services sector GDP. We also find that forecast error is minimised using both the supply side variant and the combined variant for agriculture sector GDP.

 

Keywords: Real GDP growth, Forecasting, Time Varying, Parameter Regression Model, Dynamic Factor Model, India

 

JEL Classification Codes: C32, C5, O4