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Forecasting Consumer Price Index Inflation in India: Vector Error Correction Mechanism Vs. Dynamic Factor Model Approach for Non-Stationary Time Series

Author

Listed:
  • Bhattacharya, Rudrani

    (National Institute of Public Finance and Policy)

  • Kapoor, Mrigankshi

    (Birla Institute of Technology and Science)

Abstract

Short to medium term forecasting of inflation rate is important for economic decision making by economic agents and timely implementation of monetary policy. In this study, we develop two alternative forecasting models for Year-on-Year (YOY) inflation in Consumer Price Index (CPI) in India using a large number of macroeconomic indicators. The YOY CPI inflation and its predictive indicators are found to be non-stationary and cointegrated. To address this issue, we employ Vector Error Correction Model (VECM) and Dynamic Factor Model (DFM) modified for non-stationary time series to forecast CPI inflation. We find that in terms of Root Mean Square Error (RMSE), the VECM model performs marginally better than the DFM model. However, both models are found to have the same predictive accuracy using Diebold-Mariano test.

Suggested Citation

  • Bhattacharya, Rudrani & Kapoor, Mrigankshi, 2020. "Forecasting Consumer Price Index Inflation in India: Vector Error Correction Mechanism Vs. Dynamic Factor Model Approach for Non-Stationary Time Series," Working Papers 20/323, National Institute of Public Finance and Policy.
  • Handle: RePEc:npf:wpaper:20/323
    Note: Working Paper 323, 2020
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    References listed on IDEAS

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    1. Johansen, Soren & Juselius, Katarina, 1990. "Maximum Likelihood Estimation and Inference on Cointegration--With Applications to the Demand for Money," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 52(2), pages 169-210, May.
    2. O. De Bandt & E. Michaux & C. Bruneau & A. Flageollet, 2007. "Forecasting inflation using economic indicators: the case of France," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(1), pages 1-22.
    3. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October.
    4. Carvalho, Fabia A. & Minella, André, 2012. "Survey forecasts in Brazil: A prismatic assessment of epidemiology, performance, and determinants," Journal of International Money and Finance, Elsevier, vol. 31(6), pages 1371-1391.
    5. Morten O. Ravn & Harald Uhlig, 2002. "On adjusting the Hodrick-Prescott filter for the frequency of observations," The Review of Economics and Statistics, MIT Press, vol. 84(2), pages 371-375.
    6. Stock, James H. & Watson, Mark, 2011. "Dynamic Factor Models," Scholarly Articles 28469541, Harvard University Department of Economics.
    7. S. Boragan Aruoba & Francis X. Diebold, 2010. "Real-Time Macroeconomic Monitoring: Real Activity, Inflation, and Interactions," American Economic Review, American Economic Association, vol. 100(2), pages 20-24, May.
    8. James H. Stock & Mark W. Watson, 2007. "Why Has U.S. Inflation Become Harder to Forecast?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(s1), pages 3-33, February.
    9. Motilal Bicchal & S. Raja Sethu Durai, 2019. "Rationality of inflation expectations: an interpretation of Google Trends data," Macroeconomics and Finance in Emerging Market Economies, Taylor & Francis Journals, vol. 12(3), pages 229-239, September.
    10. Johansen, Soren, 1991. "Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models," Econometrica, Econometric Society, vol. 59(6), pages 1551-1580, November.
    11. Blix, Mårten, 1999. "Forecasting Swedish Inflation With a Markov Switching VAR," Working Paper Series 76, Sveriges Riksbank (Central Bank of Sweden).
    12. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    13. Giannone, Domenico & Lenza, Michele & Momferatou, Daphne & Onorante, Luca, 2014. "Short-term inflation projections: A Bayesian vector autoregressive approach," International Journal of Forecasting, Elsevier, vol. 30(3), pages 635-644.
    14. Kapur, Muneesh, 2013. "Revisiting the Phillips curve for India and inflation forecasting," Journal of Asian Economics, Elsevier, vol. 25(C), pages 17-27.
    15. James H. Stock & Mark W. Watson, 2007. "Erratum to "Why Has U.S. Inflation Become Harder to Forecast?"," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(7), pages 1849-1849, October.
    16. James H. Stock & Mark W. Watson, 2007. "Erratum to “Why Has U.S. Inflation Become Harder to Forecast?”," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(7), pages 1849-1849, October.
    17. George Kapetanios & Gonzalo Camba-Mendez, 2005. "Forecasting euro area inflation using dynamic factor measures of underlying inflation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(7), pages 491-503.
    18. James H. Stock & Mark W. Watson, 2007. "Why Has U.S. Inflation Become Harder to Forecast?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(s1), pages 3-33, February.
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    Cited by:

    1. Patnaik, Ila & Pandey, Radhika, 2020. "Four years of the inflation targeting framework," Working Papers 20/325, National Institute of Public Finance and Policy.
    2. Badola, Shivani & Mukherjee, Sacchidananda, 2020. "Factors Influencing Access to Formal Credit of Unincorporated Enterprises in India: Analysis of NSSO's Unit-level Data," Working Papers 20/326, National Institute of Public Finance and Policy.

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    More about this item

    Keywords

    CPI Inflation ; India ; Forecasting ; Vector Error Correction Model ; Dynamic Factor Model;
    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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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