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Forecasting India’s economic growth: a time-varying parameter regression approach

Author

Listed:
  • Rudrani Bhattacharya
  • Parma Chakravartti
  • Sudipto Mundle

Abstract

Forecasting 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.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:macfem:v:12:y:2019:i:3:p:205-228
    DOI: 10.1080/17520843.2019.1603169
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    Cited by:

    1. K. M. Salah Uddin & Nishat Tanzim, 2023. "Forecasting GDP of Bangladesh Using ARIMA Model," International Journal of Business and Management, Canadian Center of Science and Education, vol. 16(6), pages 1-56, February.
    2. 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.
    3. Kaustubh, Kaustubh & Ranjan, Abhishek, 2025. "A multi-factor GDP nowcast model for India," Economic Modelling, Elsevier, vol. 147(C).
    4. Stotsky, Janet G. & Chakraborty, Lekha & Gandhi, Piyush, 2018. "Impact of Intergovernmental Fiscal Transfers on Gender Equality in India: An Empirical Analysis," Working Papers 18/240, National Institute of Public Finance and Policy.
    5. Shikha Gupta & Nand Kumar, 2023. "Time varying dynamics of globalization effect in India," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 22(1), pages 81-97, January.
    6. Barker, Jamie & Herrala, Risto, 2021. "Assessing the mid-term growth outlook for the Indian economy," BOFIT Policy Briefs 8/2021, Bank of Finland Institute for Emerging Economies (BOFIT).
    7. Shikha Gupta & Nand Kumar, 2021. "Dynamics of globalization effect in India," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 42(6), pages 1394-1406, September.

    More about this item

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • O4 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity

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