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

Pami Dua and Deepika Goel ()
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Deepika Goel: Aryabhatta College, University of Delhi

Chapter Chapter 9 in Macroeconometric Methods, 2023, pp 225-259 from Springer

Abstract: 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.

Keywords: Forecasting inflation; Factor models; FAVAR; Principal components; VECM (search for similar items in EconPapers)
JEL-codes: C32 C51 E31 E37 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-19-7592-9_9

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DOI: 10.1007/978-981-19-7592-9_9

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