Improved Estimation of Dynamic Models of Conditional Means and Variances

35 Pages Posted: 29 Sep 2020

See all articles by Weining Wang

Weining Wang

University of York; affiliation not provided to SSRN

Jeffrey M. Wooldridge

Michigan State University - Department of Economics

Mengshan Xu

London School of Economics & Political Science (LSE)

Date Written: September 11, 2020

Abstract

Modelling dynamic conditional heteroscedasticity is the daily routine in time series econometrics. We propose a weighted conditional moment estimation to potentially improve the efficiency 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 efficiency of QMLE for the location and variance parameters. Simulations are conducted to show the better performance of our estimators.

Keywords: Dynamic Models, GEE, QMLE, GARCH, Optimal Instrument, Efficiency

JEL Classification: C13,C22, C32, G00

Suggested Citation

Wang, Weining and Wang, Weining and Wooldridge, Jeffrey M. and Xu, Mengshan, Improved Estimation of Dynamic Models of Conditional Means and Variances (September 11, 2020). Available at SSRN: https://ssrn.com/abstract=3690826 or http://dx.doi.org/10.2139/ssrn.3690826

Weining Wang

University of York ( email )

Department of Economics and Related Studies Univer
York, YO10 5DD
United Kingdom

affiliation not provided to SSRN

Jeffrey M. Wooldridge

Michigan State University - Department of Economics ( email )

#211 Marshall Hall
East Lansing, MI 48824-1038
United States
517+353-5972 (Phone)

Mengshan Xu (Contact Author)

London School of Economics & Political Science (LSE) ( email )

Houghton Street
London, WC2A 2AE
United Kingdom

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