Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

IRTG1792DP2020 021

Improved Estimation of Dynamic Models of Conditional Means and Variances

Weining Wang
Jeffrey M. Wooldridge
Mengshan Xu

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.


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