An analysis of revisions in Indian GDP data

Amey Sapre and Rajeswari Sengupta

MPRA Paper from University Library of Munich, Germany

Abstract: In this paper we study revisions in the annual estimates of India’s GDP data. The objective of our analysis is to understand the revision policy adopted by the Central Statistical Organisation (CSO) and the issues therein. Using historic data, we study the magnitude and quality of revisions in the aggregate as well as the sectoral GDP series. We analyse the computation of the sectoral revised estimates and compare the extent of revision in growth rates from the first release to the final estimate. To understand the magnitude of revisions, we compute the standard deviation of revisions in growth rates for each sector and use that to build confidence bands around the initial estimates. The confidence bands provide a means to understand the extent of variation in the final growth rate estimate, and at the same time, provide a mechanism to contain revisions. Based on our analysis, we highlight some of the major issues in CSO’s revision policy. We outline possible solutions that can be implemented to improve the quality of GDP data revisions. We identify sectors with large variations in growth rates and argue that improving or changing the low quality indicators can help contain growth rate revisions and enhance the credibility of the estimates.

Keywords: GDP; National Accounts; Revisions; India (search for similar items in EconPapers)
JEL-codes: C18 E00 E01 (search for similar items in EconPapers)
Date: 2017-09-13
New Economics Papers: this item is included in nep-mac
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Citations: View citations in EconPapers (4)

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https://mpra.ub.uni-muenchen.de/83390/1/MPRA_paper_81340.pdf revised version (application/pdf)

Related works:
Journal Article: Analysis of Revisions in Indian GDP Data (2017)
Working Paper: An Analysis of Revisions in Indian GDP Data (2017)
Working Paper: An analysis of revisions in Indian GDP data (2017)
Working Paper: An analysis of revisions in Indian GDP data (2017)
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