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Testing for Conditional Heteroscedasticity in the Components of Inflation

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  • Broto Carmen

    ()
    (Banco de España)

  • Ruiz Esther

    ()
    (Universidad Carlos III de Madrid)

Abstract

In this paper we propose a model for monthly inflation with stochastic trend, seasonal and transitory components with QGARCH disturbances. This model distinguishes whether the long-run or short-run components are heteroscedastic. Furthermore, the uncertainty associated with these components may increase with the level of inflation as postulated by Friedman. We propose to use the differences between the autocorrelations of squares and the squared autocorrelations of the auxiliary residuals to identify heteroscedastic components. We show that conditional heteroscedasticity truly present in the data can be rejected when looking at the correlations of standardized residuals while the autocorrelations of auxiliary residuals have more power to detect conditional heteroscedasticity. Furthermore, the proposed statistics can help to decide which component is heteroscedastic. Their finite sample performance is compared with that of a Lagrange Multiplier test by means of Monte Carlo experiments. Finally, we use auxiliary residuals to detect conditional heteroscedasticity in ten monthly inflation series.

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Bibliographic Info

Article provided by De Gruyter in its journal Studies in Nonlinear Dynamics & Econometrics.

Volume (Year): 13 (2009)
Issue (Month): 2 (May)
Pages: 1-30

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Handle: RePEc:bpj:sndecm:v:13:y:2009:i:2:n:4

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Cited by:
  1. Josu Arteche, 2012. "Standard and seasonal long memory in volatility: an application to Spanish inflation," Empirical Economics, Springer, vol. 42(3), pages 693-712, June.
  2. Carmen Broto, 2008. "Inflation targeting in Latin America: Empirical analysis using GARCH models," Banco de Espa�a Working Papers 0826, Banco de Espa�a.
  3. Pellegrini, Santiago & Ruiz, Esther & Espasa, Antoni, 2011. "Prediction intervals in conditionally heteroscedastic time series with stochastic components," International Journal of Forecasting, Elsevier, vol. 27(2), pages 308-319.

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