Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/230827 
Erscheinungsjahr: 
2020
Schriftenreihe/Nr.: 
IRTG 1792 Discussion Paper No. 2020-021
Verlag: 
Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series", Berlin
Zusammenfassung: 
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.
JEL: 
C00
Dokumentart: 
Working Paper

Datei(en):
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