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Forecasting Inflation in Tunisia Using Dynamic Factors Model

Author

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  • AMMOURI, Bilel
  • TOUMI, Hassen
  • Zitouna, Habib

Abstract

This work presents a forecasting inflation model using a monthly database. Conventional models for forecasting inflation use a small number of macroeconomic variables. In the context of globalization and dependent economic world, models have to account a large number of information. This model is the goal of recent research in the various industrialized countries as well as developing countries. With Dynamic Factors Model the forecast values are closer to actual inflation than those obtained from conventional models in the short term. In our research we devise the inflation in to “free inflation and administered inflation” and we test the performance of the DFM in different types of inflation namely administered and free inflation. We found that dynamic factors model leads to substantial forecasting improvements over simple benchmark regressions.

Suggested Citation

  • AMMOURI, Bilel & TOUMI, Hassen & Zitouna, Habib, 2015. "Forecasting Inflation in Tunisia Using Dynamic Factors Model," MPRA Paper 65514, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:65514
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    File URL: https://mpra.ub.uni-muenchen.de/68455/8/MPRA_paper_68455.pdf
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    References listed on IDEAS

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    1. Arthur F. Burns, 1950. "New Facts on Business Cycles," NBER Books, National Bureau of Economic Research, Inc, number annu50-1, June.
    2. Robert J. Gordon, 1997. "The Time-Varying NAIRU and Its Implications for Economic Policy," Journal of Economic Perspectives, American Economic Association, vol. 11(1), pages 11-32, Winter.
    3. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    4. Jeff Fuhrer & George Moore, 1995. "Inflation Persistence," The Quarterly Journal of Economics, Oxford University Press, vol. 110(1), pages 127-159.
    5. Fuhrer, Jeffrey C., 2010. "Inflation Persistence," Handbook of Monetary Economics, in: Benjamin M. Friedman & Michael Woodford (ed.),Handbook of Monetary Economics, edition 1, volume 3, chapter 9, pages 423-486, Elsevier.
    6. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October.
    7. Yock Y. Chong & David F. Hendry, 1986. "Econometric Evaluation of Linear Macro-Economic Models," Review of Economic Studies, Oxford University Press, vol. 53(4), pages 671-690.
    8. Galí, Jordi & Gertler, Mark, 1999. "Inflation Dynamics: A Structural Economic Analysis," CEPR Discussion Papers 2246, C.E.P.R. Discussion Papers.
    9. Giannone, Domenico & Reichlin, Lucrezia & Sala, Luca, 2006. "VARs, common factors and the empirical validation of equilibrium business cycle models," Journal of Econometrics, Elsevier, vol. 132(1), pages 257-279, May.
    10. Amengual, Dante & Watson, Mark W., 2007. "Consistent Estimation of the Number of Dynamic Factors in a Large N and T Panel," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 91-96, January.
    11. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
    12. Calvo, Guillermo A., 1983. "Staggered prices in a utility-maximizing framework," Journal of Monetary Economics, Elsevier, vol. 12(3), pages 383-398, September.
    13. Filardo, Andrew J. & Gordon, Stephen F., 1998. "Business cycle durations," Journal of Econometrics, Elsevier, vol. 85(1), pages 99-123, July.
    14. Gali, Jordi & Gertler, Mark, 1999. "Inflation dynamics: A structural econometric analysis," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 195-222, October.
    15. James H. Stock & Mark W. Watson, 1993. "Business Cycles, Indicators, and Forecasting," NBER Books, National Bureau of Economic Research, Inc, number stoc93-1, June.
    16. Stock, James H. & Watson, Mark W. (ed.), 1993. "Business Cycles, Indicators, and Forecasting," National Bureau of Economic Research Books, University of Chicago Press, number 9780226774886, March.
    17. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-162, April.
    18. Alexei Onatski, 2010. "Determining the Number of Factors from Empirical Distribution of Eigenvalues," The Review of Economics and Statistics, MIT Press, vol. 92(4), pages 1004-1016, November.
    19. James H. Stock & Mark W. Watson, 1993. "Introduction to "Business Cycles, Indicators and Forecasting"," NBER Chapters, in: Business Cycles, Indicators, and Forecasting, pages 1-10, National Bureau of Economic Research, Inc.
    20. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
    21. Watson, Mark W. & Engle, Robert F., 1983. "Alternative algorithms for the estimation of dynamic factor, mimic and varying coefficient regression models," Journal of Econometrics, Elsevier, vol. 23(3), pages 385-400, December.
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    More about this item

    Keywords

    Inflation; PCA; VAR; Dynamic Factors Model; Kalman Filter; algorithmic EM; Space-state; forecast.;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation

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