Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/203415
Authors: 
Iyer, Tara
Gupta, Abhijit Sen
Year of Publication: 
2019
Series/Report no.: 
ADB Economics Working Paper Series 573
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.
Subjects: 
Bayesian vector autoregressions
GDP growth
India
time series forecasting
JEL: 
C11
C32
C53
F43
Persistent Identifier of the first edition: 
Creative Commons License: 
https://creativecommons.org/licenses/by/3.0/igo/
Document Type: 
Working Paper
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