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- W2972699304 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." @default.
- W2972699304 created "2019-09-19" @default.
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- W2972699304 date "2019-07-09" @default.
- W2972699304 modified "2023-10-03" @default.
- W2972699304 title "Adaptive inference for a semiparametric generalized autoregressive conditional heteroskedasticity model" @default.
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- W2972699304 doi "https://doi.org/10.48550/arxiv.1907.04147" @default.
- W2972699304 hasPublicationYear "2019" @default.
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