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A nowcast of 2021-22 GDP growth and forecast for 2022-23 based on a Factor Augmented Time Varying Coefficients Regression Model

Author

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  • Bhattacharya, Rudrani

    (National Institute of Public Finance and Policy)

  • Mundle, Sudipto

Abstract

In this paper we have used our recently updated Factor Augmented Time Varying Coefficients Regression (FA-TVCR) model (Bhattacharya, Chakravartti and Mundle, 2019; Bhattacharya, Bhandari and Mundle 2021) to nowcast GDP growth for 2021-22 and forecast it for the year 2022-23. Our GDP growth nowcast for 2021-22 is 9.9 per cent, somewhat higher than the RBI projection of 9.5 per cent. The forecast for 2022-23 is 5.2 per cent. Factoring in an inflation rate of 5 per cent, this would translate to a nominal GDP growth rate of 10.2 per cent which is lower than the RBI projection of 12.3-13 percent but slightly higher than the 15th Finance Commission projection of 9.5 percent.

Suggested Citation

  • Bhattacharya, Rudrani & Mundle, Sudipto, 2021. "A nowcast of 2021-22 GDP growth and forecast for 2022-23 based on a Factor Augmented Time Varying Coefficients Regression Model," Working Papers 21/361, National Institute of Public Finance and Policy.
    • Handle: RePEc:npf:wpaper:21/361
    Note: Working Paper 361, 2021
    as

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    References listed on IDEAS

    as
    1. Rudrani Bhattacharya & Parma Chakravartti & Sudipto Mundle, 2019. "Forecasting India’s economic growth: a time-varying parameter regression approach," Macroeconomics and Finance in Emerging Market Economies, Taylor & Francis Journals, vol. 12(3), pages 205-228, September.
    2. Rudrani Bhattacharya & Bornali Bhandari & Sudipto Mundle, 2021. "Nowcasting India's Quarterly GDP Growth: A Factor Augmented Time-Varying Coefficient Regression Model (FA-TVCRM)," NCAER Working Papers 130, National Council of Applied Economic Research.
    3. Sonalde Desai & Neerad Deshmukh & Santanu Pramanik, 2021. "Precarity in a Time of Uncertainty: Gendered Employment Patterns during the Covid-19 Lockdown in India," Feminist Economics, Taylor & Francis Journals, vol. 27(1-2), pages 152-172, April.
    4. Chang-Jin Kim & Charles R. Nelson, 1999. "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262112388, December.
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