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

  • Cathy Chen

    ()

  • Richard Gerlach

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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File URL: http://hdl.handle.net/10.1007/s00180-012-0346-9
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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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