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Forecasting India’s Inflation in a Data-Rich Environment: A FAVAR Study

In: Macroeconometric Methods

Author

Listed:
  • Pami Dua

    (University of Delhi)

  • Deepika Goel

    (Aryabhatta College, University of Delhi)

Abstract

The study develops a multivariate Factor-Augmented VAR (FAVAR) model of inflation for India to forecast India’s inflation. The analysis covers both WPI and CPI measures of inflation. Factors are extracted for determinants of inflation such as output, monetary and credit indicators, interest rate, fiscal indicators, exchange rate, minimum support prices, food inflation, rainfall and foreign inflation using 117 economic time series. The study further evaluates the forecasting performance of the FAVAR model vis-à-vis the VECM model and univariate ARIMA/ARIMA-GARCH models. The models are estimated using monthly data covering the period 2001:05 to 2016:06, and out-of-sample forecasts are generated for the period 2016:07 to 2018:01. The FAVAR model for both measures of inflation suggests that in terms of normalized generalized variance decompositions, maximum variation in WPI inflation in India is explained by exchange rate factor, followed by Minimum Support Price inflation and then by inflation expectations, whereas maximum variation in CPI inflation is explained by expected inflation followed by monetary and credit factor, fiscal factor and finally by output factor. The forecasting exercise suggests that FAVAR emerges as the best model in terms of various forecast accuracy measures. The Modified Diebold Mariano test also suggests that the forecasts from the multivariate FAVAR model are significantly more accurate than the univariate ARIMA-GARCH model and the multivariate VECM model for almost all horizons.

Suggested Citation

  • Pami Dua & Deepika Goel, 2023. "Forecasting India’s Inflation in a Data-Rich Environment: A FAVAR Study," Springer Books, in: Pami Dua (ed.), Macroeconometric Methods, chapter 0, pages 225-259, Springer.
    • Handle: RePEc:spr:sprchp:978-981-19-7592-9_9
    DOI: 10.1007/978-981-19-7592-9_9
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    More about this item

    Keywords

    Forecasting inflation; Factor models; FAVAR; Principal components; VECM;
    All these keywords.

    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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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