Adaptive inference for a semiparametric generalized autoregressive conditional heteroskedasticity model

Feiyu Jiang, Dong Li and Ke Zhu ()

Journal of Econometrics, 2021, vol. 224, issue 2, 306-329

Abstract: This paper considers a semiparametric generalized autoregressive conditional heteroskedasticity (S-GARCH) model. For this model, we first estimate the time-varying long run component for unconditional variance by the kernel estimator, and then estimate the non-time-varying parameters in GARCH-type 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 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 targeting method when the S-GARCH model is stationary.

Keywords: Adaptive inference; Lagrange multiplier test; Portmanteau test; QMLE; Semiparametric BEKK model; Semiparametric GARCH model (search for similar items in EconPapers)
JEL-codes: C12 C14 C58 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:224:y:2021:i:2:p:306-329

DOI: 10.1016/j.jeconom.2020.10.007

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