Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/319359 
Erscheinungsjahr: 
2024
Quellenangabe: 
[Journal:] Journal of Time Series Analysis [ISSN:] 1467-9892 [Volume:] 46 [Issue:] 3 [Publisher:] John Wiley & Sons, Ltd [Place:] Oxford, UK [Year:] 2024 [Pages:] 458-490
Verlag: 
John Wiley & Sons, Ltd, Oxford, UK
Zusammenfassung: 
Using ‘working’ assumptions on conditional third and fourth moments of errors, we propose a method of moments estimator that can have improved efficiency over the popular Gaussian quasi‐maximum likelihood estimator (GQMLE). Higher‐order moment assumptions are not needed for consistency – we only require the first two conditional moments to be correctly specified – but the optimal instruments are derived under these assumptions. The working assumptions allow both asymmetry in the distribution of the standardized errors as well as fourth moments that can be smaller or larger than that of the Gaussian distribution. The approach is related to the generalized estimation equations (GEE) approach – which seeks the improvement of estimators of the conditional mean parameters by making working assumptions on the conditional second moments. We derive the asymptotic distribution of the new estimator and show that it does not depend on the estimators of the third and fourth moments. A simulation study shows that the efficiency gains over the GQMLE can be non‐trivial.
Schlagwörter: 
Dynamic models
GEE
QMLE
GARCH
optimal instrument
efficiency
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