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Semi-parametric quantile estimation for double threshold autoregressive models with heteroskedasticity

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  • Cathy Chen

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  • Richard Gerlach

Abstract

Compared to the conditional mean or median, conditional quantiles provide a more comprehensive picture of a variable in various scenarios. A semi-parametric quantile estimation method for a double threshold auto-regression with exogenous regressors and heteroskedasticity is considered, allowing representation of both asymmetry and volatility clustering. As such, GARCH dynamics with nonlinearity are added to a nonlinear time series regression model. An adaptive Bayesian Markov chain Monte Carlo scheme, exploiting the link between the quantile loss function and the asymmetric-Laplace distribution, is employed for estimation and inference, simultaneously estimating and accounting for nonlinear heteroskedasticity plus unknown threshold limits and delay lags. A simulation study illustrates sampling properties of the method. Two data sets are considered in the empirical applications: modelling daily maximum temperatures in Melbourne, Australia; and exploring dynamic linkages between financial markets in the US and Hong Kong. Copyright Springer-Verlag 2013

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Bibliographic Info

Article provided by Springer in its journal Computational Statistics.

Volume (Year): 28 (2013)
Issue (Month): 3 (June)
Pages: 1103-1131

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Handle: RePEc:spr:compst:v:28:y:2013:i:3:p:1103-1131

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Related research

Keywords: Asymmetric Laplace distribution; Nonlinear time series; MCMC; GARCH; Quantile regression;

References

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Cited by:
  1. Cathy Chen & Simon Lin & Philip Yu, 2012. "Smooth Transition Quantile Capital Asset Pricing Models with Heteroscedasticity," Computational Economics, Society for Computational Economics, vol. 40(1), pages 19-48, June.

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