Pedersen, Rasmus Søndergaard (2017): Robust inference in conditionally heteroskedastic autoregressions.
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Abstract
We consider robust inference for an autoregressive parameter in a stationary autoregressive model with GARCH innovations when estimation is based on least squares estimation. As the innovations exhibit GARCH, they are by construction heavytailed with some tail index $\kappa$. The rate of consistency as well as the limiting distribution of the least squares estimator depend on $\kappa$. In the spirit of Ibragimov and Müller (“tstatistic based correlation and heterogeneity robust inference”, Journal of Business & Economic Statistics, 2010, vol. 28, pp. 453468), we consider testing a hypothesis about a parameter based on a Student’s tstatistic for a fixed number of subsamples of the original sample. The merit of this approach is that no knowledge about the value of $\kappa$ nor about the rate of consistency and the limiting distribution of the least squares estimator is required. We verify that the onesided ttest is asymptotically a level $\alpha$ test whenever $\alpha \le $ 5% uniformly over $\kappa \ge 2$, which includes cases where the innovations have infinite variance. A simulation experiment suggests that the finitesample properties of the test are quite good.
Item Type:  MPRA Paper 

Original Title:  Robust inference in conditionally heteroskedastic autoregressions 
Language:  English 
Keywords:  ttest, ARGARCH, regular variation, least squares estimation 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C12  Hypothesis Testing: General C  Mathematical and Quantitative Methods > C2  Single Equation Models ; Single Variables > C22  TimeSeries Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes C  Mathematical and Quantitative Methods > C4  Econometric and Statistical Methods: Special Topics > C46  Specific Distributions ; Specific Statistics C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C51  Model Construction and Estimation 
Item ID:  90609 
Depositing User:  Dr Rasmus Søndergaard Pedersen 
Date Deposited:  18 Dec 2018 06:42 
Last Modified:  27 Sep 2019 04:25 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/90609 
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Robust inference in conditionally heteroskedastic autoregressions. (deposited 17 Oct 2017 16:56)
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