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A Multivariate Perspective for Modeling and Forecasting Inflation's Conditional Mean and Variance

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  • Matteo Barigozzi
  • Marco Capasso

Abstract

We test the importance of multivariate information for modelling and forecasting in- flation's conditional mean and variance. In the literature, the existence of inflation's conditional heteroskedasticity has been debated for years, as it seemed to appear only in some datasets and for some lag lengths. This phenomenon might be due to the fact that inflation depends on a linear combination of economy-wide dynamic common fac- tors, some of which are conditionally heteroskedastic and some are not. Modelling the conditional heteroskedasticity of the common factors can thus improve the forecasts of inflation's conditional mean and variance. Moreover, it allows to detect and predict con- ditional correlations between inflation and other macroeconomic variables, correlations that might be exploited when planning monetary policies. The Dynamic Factor GARCH (DF-GARCH) by Alessi et al. [2006] is used here to exploit the relations between inflation and the other macroeconomic variables for inflation fore- casting purposes. The DF-GARCH is a dynamic factor model as the one by Forni et al. [2005], with the addition of an equation for the evolution of static factors as in Giannone et al. [2004] and the assumption of heteroskedastic dynamic factors. When comparing the Dynamic Factor GARCH with univariate models and with the classical dynamic factor models, the DF-GARCH is able to provide better forecasts both of inflation and of its conditional variance.

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Bibliographic Info

Paper provided by Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy in its series LEM Papers Series with number 2007/21.

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Date of creation: 01 Oct 2007
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Handle: RePEc:ssa:lemwps:2007/21

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Related research

Keywords: Inflation; Factor Models; GARCH;

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References

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  1. Jean Boivin & Serena Ng, 2005. "Understanding and Comparing Factor-Based Forecasts," International Journal of Central Banking, International Journal of Central Banking, vol. 1(3), December.
  2. Mario Forni & Domenico Giannone & Marco Lippi & Lucrezia Reichlin, 2008. "Opening the Black Box: Structural Factor Models with Large Cross-Sections," Working Papers ECARES 2008_036, ULB -- Universite Libre de Bruxelles.
  3. Lucrezia Reichlin & Domenico Giannone & Luca Sala, . "Monetary policy in real time," ULB Institutional Repository 2013/10177, ULB -- Universite Libre de Bruxelles.
    • Domenico Giannone & Lucrezia Reichlin & Luca Sala, 2005. "Monetary Policy in Real Time," NBER Chapters, in: NBER Macroeconomics Annual 2004, Volume 19, pages 161-224 National Bureau of Economic Research, Inc.
  4. Grier, Kevin B. & Perry, Mark J., 1998. "On inflation and inflation uncertainty in the G7 countries," Journal of International Money and Finance, Elsevier, vol. 17(4), pages 671-689, August.
  5. Ball, Laurence, 1992. "Why does high inflation raise inflation uncertainty?," Journal of Monetary Economics, Elsevier, vol. 29(3), pages 371-388, June.
  6. D''Agostino, Antonello & Giannone, Domenico, 2007. "Comparing Alternative Predictors Based on Large-Panel Factor Models," CEPR Discussion Papers 6564, C.E.P.R. Discussion Papers.
  7. Lucia Alessi & Matteo Barigozzi & Marco Capasso, 2006. "Dynamic Factor GARCH: Multivariate Volatility Forecast for a Large Number of Series," LEM Papers Series 2006/25, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
  8. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2003. "The Generalized Dynamic Factor Model. One-Sided Estimation and Forecasting," LEM Papers Series 2003/13, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
  9. Kenneth D. West & Todd Clark, 2006. "Approximately Normal Tests for Equal Predictive Accuracy in Nested Models," NBER Technical Working Papers 0326, National Bureau of Economic Research, Inc.
  10. Jushan Bai & Serena Ng, 2000. "Determining the Number of Factors in Approximate Factor Models," Econometric Society World Congress 2000 Contributed Papers 1504, Econometric Society.
  11. Lucia Alessi & Matteo Barigozzi & Marco Capasso, 2007. "A Robust Criterion for Determining the Number of Static Factors in Approximate Factor Models," LEM Papers Series 2007/19, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
  12. Kilian, Lutz & Manganelli, Simone, 2007. "The Central Banker as a Risk Manager: Estimating the Federal Reserve's Preferences under Greenspan," CEPR Discussion Papers 6031, C.E.P.R. Discussion Papers.
  13. Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
  14. Francis X. Diebold & Robert S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
  15. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-50, July.
  16. Stilianos Fountas & Menelaos Karanasos & Marika Karanassou, 2000. "A GARCH Model of Inflation and Inflation Uncertainty with Simultaneous Feedback," Working Papers 0047, National University of Ireland Galway, Department of Economics, revised 2000.
  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 147-62, April.
  18. Friedman, Milton, 1977. "Nobel Lecture: Inflation and Unemployment," Journal of Political Economy, University of Chicago Press, vol. 85(3), pages 451-72, June.
  19. repec:fth:galeco:47 is not listed on IDEAS
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