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Artificial regression testing in the GARCH-in-mean model

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  • Riccardo Lucchetti
  • Eduardo Rossi

Abstract

The issue of finite-sample inference in Generalised Autoregressive Conditional Heteroskedasticity (GARCH)-like models has seldom been explored in the theoretical literature, although its potential relevance for practitioners is obvious. In some cases, asymptotic theory may provide a very poor approximation to the actual distribution of the estimators in finite samples. The aim of this paper is to propose the application of the so-called double length regressions (DLR) to GARCH-in-mean models for inferential purposes. As an example, we focus on the issue of Lagrange Multiplier tests on the risk premium parameter. Simulation evidence suggests that DLR-based Lagrange Multiplier (LM) test statistics provide a much better testing framework than the more commonly used LM tests based on the outer product of gradients (OPG) in terms of actual test size, especially when the GARCH process exhibits high persistence in volatility. This result is consistent with previous studies on the subject. Copyright 2005 Royal Economic Society

Suggested Citation

  • Riccardo Lucchetti & Eduardo Rossi, 2005. "Artificial regression testing in the GARCH-in-mean model," Econometrics Journal, Royal Economic Society, vol. 8(3), pages 306-322, December.
  • Handle: RePEc:ect:emjrnl:v:8:y:2005:i:3:p:306-322
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    Cited by:

    1. Riccardo LUCCHETTI & Giulio PALOMBA, 2006. "Forecasting US bond yields at weekly frequency," Working Papers 261, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.

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