Model Averaging in a Multiplicative Heteroscedastic Model

In recent years, the body of literature on frequentist model averagingin econometrics has grown significantly. Most of this work focuses onmodels with different mean structures but leaves out the varianceconsideration.  In... [ view full abstract ]

In recent years, the body of literature on frequentist model averaging

in econometrics has grown significantly. Most of this work focuses on

models with different mean structures but leaves out the variance

consideration.  In this paper, 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.