Existing model averaging methods are generally based on ordinary least squares (OLS) estimators. However, it is well known that the James–Stein (JS) estimator dominates the OLS estimator under quadratic loss, provided that the dimension of coefficient is larger than two. Thus, we focus on...

The past decade witnessed a literature on model averaging by frequentist methods. For the most part, the asymptotic optimality of various existing frequentist model averaging estimators has been established under i.i.d. errors. Recently, Hansen and Racine [Hansen, B.E., Racine, J., 2012....

This paper proposes a model averaging method, the generalized Mallows’ Cp (GC) method, which works well for heteroskedastic models. Under some regularity conditions, we provide a feasible form of the GC method and show that the GC method has asymptotic optimality not only as a model averaging...

This paper proposes a method of averaging generalized least squares (GLS) estimators for linear regression models with heteroskedastic errors. We derive two kinds of Mallows' Cp criteria, calculated from the estimates of the mean of the squared errors of the tted value based on the averaged GLS...

Volatility forecasting is an important issue in empirical finance. In this paper, the main purpose is to apply the model averaging techniques to reduce volatility model uncertainty and improve volatility forecasting. Six GARCH-type models are considered as candidate models for model averaging....

This paper examines the intensity of financial crises during the 1990s with a view to informing crisis prevention and mitigation policies. We compare the performance of a full Bayesian and an information-theoretic approach in addressing the econometric problems posed by the lack of a unifying...