Mildly explosive autoregression under stationary conditional heteroskedasticity

Stelios Arvanitis and Tassos Magdalinos
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Stelios Arvanitis: Athens University of Economics and Business, Greece
Tassos Magdalinos: University of Southampton, UK; Rimini Centre for Economic Analysis

Working Paper series from Rimini Centre for Economic Analysis

Abstract: A limit theory is developed for mildly explosive autoregressions under stationary (weakly or strongly dependent) conditionally heteroskedastic errors. The conditional variance process is allowed to be stationary, integrable and mixingale, thus encompassing general classes of GARCH type or stochastic volatility models. No mixing conditions nor moments of higher order than 2 are assumed for the innovation process. As in Magdalinos (2012), we find that the asymptotic behaviour of the sample moments is affected by the memory of the innovation process both in the form of the limiting distribution and, in the case of long range dependence, the rate of convergence, while conditional heteroskedasticity affects only the asymptotic variance. These effects are cancelled out in least squares regression theory and thereby the Cauchy limit theory of Phillips and Magdalinos (2007a) remains invariant to a wide class of stationary conditionally heteroskedastic innovations processes.

Keywords: Central limit theory; Explosive autoregression; Long Memory; Conditional heteroskedasticity; GARCH; mixingale; Cauchy distribution (search for similar items in EconPapers)
Date: 2018-06
New Economics Papers: this item is included in nep-ecm and nep-ets
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Citations: View citations in EconPapers (10)

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