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A0219
Title: Model averaging in a multiplicative heteroscedastic model Authors:  Alan Wan - City University of Hong Kong (Hong Kong) [presenting]
Abstract: In recent years, the literature of frequentist model averaging in econometrics has grown significantly. Models with different mean structures have been considered, but variance considerations have been left out. We consider a regression model with multiplicative heteroscedasticity and develop a model averaging method that combines maximum likelihood estimators of unknown parameters in both the mean and variance functions of the model. Our weight choice criterion is based on a minimisation of a plug-in estimator of the model average estimator's squared prediction risk. We prove that the new estimator possesses an asymptotic optimality property. Our investigation of finite-sample performance by simulations demonstrates that the new estimator frequently exhibits very favourable properties compared to some existing heteroscedasticity-robust model average estimators. The model averaging method hedges against the selection of very bad models and serves as a remedy to variance function mis-specification, which often discourages practitioners from modeling heteroscedasticity altogether. The proposed model average estimator is applied to the analysis of two data sets on housing and economic growth.