Closed-form estimators for finite-order ARCH models as simple and competitive alternatives to QMLE
Strong consistency and (weak) distributional convergence to highly non-Gaussian limits are established for closed-form, two stage least squares (TSLS) estimators of linear and threshold ARCH ( ) models, with special attention paid to the ARCH (1) and threshold ARCH (1) cases. Conditions supporting t...
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| Veröffentlicht in: | Studies in nonlinear dynamics and econometrics 2018-12, Vol.22 (5), p.1-25 |
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| creator | Prono, Todd |
| description | Strong consistency and (weak) distributional convergence to highly non-Gaussian limits are established for closed-form, two stage least squares (TSLS) estimators of linear and threshold ARCH (
) models, with special attention paid to the ARCH (1) and threshold ARCH (1) cases. Conditions supporting these results include (relatively) mild moment existence criteria that enjoy empirical support. These conditions are not shared by competing estimators like OLS. Identification of the TSLS estimators depends on asymmetry, either in the model’s rescaled errors or in the conditional variance function. Monte Carlo studies reveal TSLS estimation can sizably outperform quasi maximum likelihood (QML) in small samples and even best recently proposed two-step estimators specifically designed to enhance the efficiency of QML. |
| doi_str_mv | 10.1515/snde-2017-0070 |
| format | Article |
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| ispartof | Studies in nonlinear dynamics and econometrics, 2018-12, Vol.22 (5), p.1-25 |
| issn | 1081-1826 1558-3708 |
| language | eng |
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| source | De Gruyter journals |
| subjects | ARCH Asymmetry C13 C22 C58 closed form estimation Closed form solutions Computer simulation Convergence Econometrics Estimators Exact solutions heavy tails instrumental variables Linear programming Mathematical analysis Mean square errors Monte Carlo simulation Normal distribution regular variation threshold ARCH two stage least squares |
| title | Closed-form estimators for finite-order ARCH models as simple and competitive alternatives to QMLE |
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