Arbeitspapier
Improved Estimation of Dynamic Models of Conditional Means and Variances
Modelling dynamic conditional heteroscedasticity is the daily routine in time series econometrics. We propose a weighted conditional moment estimation to potentially improve the eciency of the QMLE (quasi maximum likelihood estimation). The weights of conditional moments are selected based on the analytical form of optimal instruments, and we nominally decide the optimal instrument based on the third and fourth moments of the underlying error term. This approach is motivated by the idea of general estimation equations (GEE). We also provide an analysis of the eciency of QMLE for the location and variance parameters. Simulations and applications are conducted to show the better performance of our estimators.
- Sprache
-
Englisch
- Erschienen in
-
Series: IRTG 1792 Discussion Paper ; No. 2020-021
- Klassifikation
-
Wirtschaft
Mathematical and Quantitative Methods: General
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Wang, Weining
Wooldridge, Jeffrey M.
Xu, Mengshan
- Ereignis
-
Veröffentlichung
- (wer)
-
Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"
- (wo)
-
Berlin
- (wann)
-
2020
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:42 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Wang, Weining
- Wooldridge, Jeffrey M.
- Xu, Mengshan
- Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"
Entstanden
- 2020
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