The method of model averaging has become an important tool to deal with model
uncertainty, for example in situations where a large amount of different theories exist, as are …

Abstract Model averaging has become a popular method of estimation, following increasing
evidence that model selection and estimation should be treated as one joint procedure …

This paper proposes a new penalized time-varying model averaging method to determine
optimal time-varying combination weights for candidate models, which avoids over-fitting …

This study considers the ultrahigh-dimensional prediction problem in the presence of
responses missing at random. A two-step model-averaging procedure is proposed to …

Consider a regression model with infinitely many parameters and time series errors. We are
interested in choosing weights for averaging across generalized least squares (GLS) …

In this article, we present a comprehensive study of asymptotic optimality of least squares
model averaging methods. The concept of asymptotic optimality is that in a large-sample …

Abstract Model averaging has a rich history dating from its use for combining forecasts from
time-series models (Bates and Granger) and presents a compelling alternative to model …

This paper is concerned with model averaging estimation for conditional volatility models.
Given a set of candidate models with different functional forms, we propose a model …

We consider the problem of model averaging over a set of semiparametric varying
coefficient models where the varying coefficients can be functions of continuous and …

This article considers the problem of model averaging for regression models that can be
nonlinear in their parameters and variables. We consider a nonlinear model averaging …