Abstract
This study develops a framework to forecast India’s gross domestic product growth on a quarterly frequency from 2004 to 2018. The models, which are based on real and monetary sector descriptions of the Indian economy, are estimated using Bayesian vector autoregression (BVAR) techniques. The real sector groups of variables include domestic aggregate demand indicators and foreign variables, while the monetary sector groups specify the underlying inflationary process in terms of the consumer price index (CPI) versus the wholesale price index given India’s recent monetary policy regime switch to CPI inflation targeting. The predictive ability of over 3,000 BVAR models is assessed through a set of forecast evaluation statistics and compared with the forecasting accuracy of alternate econometric models including unrestricted and structural VARs. Key findings include that capital flows to India and CPI inflation have high informational content for India’s GDP growth. The results of this study provide suggestive evidence that quarterly BVAR models of Indian growth have high predictive ability.
Highlights
This study seeks to develop an appropriate forecasting model to predict India’s gross domestic product (GDP) growth
The high predictive power of the Bayesian vector autoregression (BVAR) holds robust to major shocks such as the global financial crisis (GFC) and the Indian demonetization, structural changes including the monetary policy regime switch from a multiple indicators approach to inflation targeting, revisions in the Indian national accounts data, as well as several variations in the start and length of the estimation and validation periods
The sample period begins in the first quarter of 2004. This is around the time when GDP growth in India began to accelerate, and India started becoming more integrated with the global economy
Summary
A.2 Top 50 Bayesian Vector Autoregression Forecasting Models of Gross Domestic Product 30. Top 10 Vector Autoregression Forecasting Models of Gross Domestic Product Growth 33. Structural Vector Autoregression Forecasting Models of Gross Domestic Product Growth 34. Dynamic Bayesian Vector Autoregression and Autoregressive Integrated Moving Average 35. Top 50 Bayesian Vector Autoregression Forecasting Models of Gross Domestic Product 35 A.9 Top 50 Bayesian Vector Autoregression Forecasting Models of Gross Domestic Product 35
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