Abstract
ABSTRACTForecasting 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 tried, namely, Demand-side, Supply-side and Combined models. We also find that TVPR model consistently outperforms constant parameter principal component augmented regression model and Dynamic Factor Model in terms of forecasting performance for all the three specifications.
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