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

Cathy W. S. Chen () and Richard Gerlach

Computational Statistics, 2013, vol. 28, issue 3, 1103-1131

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

Keywords: Asymmetric Laplace distribution; Nonlinear time series; MCMC; GARCH; Quantile regression (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:spr:compst:v:28:y:2013:i:3:p:1103-1131

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DOI: 10.1007/s00180-012-0346-9

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