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Activity Number:
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346
- Time Series: Stationarity, Non-Stationarity, Cointegration, ARCH Models, and GARCH Models
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Type:
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Contributed
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Date/Time:
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Wednesday, August 5, 2020 : 10:00 AM to 2:00 PM
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Sponsor:
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Business and Economic Statistics Section
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Abstract #312290
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Title:
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Adaptive Inference for a Semiparametric GARCH Model
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Author(s):
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Feiyu Jiang* and Ke Zhu and Dong Li
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Companies:
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Tsinghua University and The University of Hong Kong and Tsinghua University
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Keywords:
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Adaptive inference;
Bayesian information criterion;
Lagrange multiplier test;
Semiparametric;
GARCH
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Abstract:
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We consider a semiparametric generalized autoregressive conditional heteroscedastic (S-GARCH) model. For this model, we first estimate the time-varying long run component by the kernel estimator, and then estimate the non-time-varying parameters in short run component by the quasi maximum likelihood estimator (QMLE).We show that the QMLE is asymptotically normal with the parametric convergence rate. Next, we provide a consistent Bayesian information criterion for order selection. Furthermore, we construct a Lagrange multiplier test for linear parameter constraint and a portmanteau test for model checking, and obtain their asymptotic null distributions. Our entire statistical inference procedure works for the non-stationary data with two important features: first, our QMLE and two tests are adaptive to the unknown form of the long run component; second, our QMLE and two tests share the same efficiency and testing power as those in variance target method when the S-GARCH model is stationary.
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Authors who are presenting talks have a * after their name.
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