A recent strand of the literature has proposed stochastic time-varying coefficient models for modelling structural change in the macroeconomy under both exogeneity and endogeneity. Subsequently, a new class of kernel based non-parametric estimators has been introduced for these models. These...

The inefficiency term in stochastic frontier models is usually assumed to have positive skewness; but when this assumption is not met, efficiency scores are overestimated. Potential endogeneity of model regressors poses an additional empirical challenge and greatly hinders identification of...

In the aftermath of the Global Financial Crisis, some risk management practitioners have advocated wider adoption of Bayesian inference to replace Value- at-Risk (VaR) models in order to minimize risk failures. Despite its limitations, the Bayesian methodology has significant advantages. Just...

To address the unknown nature of probability-­sampling models, in this paper we use information theoretic concepts and the Cressie-Read (CR) family of information divergence measures to produce a flexible family of probability distributions, likelihood function relationships, estimators, and...

Model selection and model combination is a general problem in many areas. Especially, when we have several different candidate models and also have gathered a new data set, we want to construct a more accurate and precise model in order to help predict future events. In this paper, we propose a...

A general statistical modeling problem is that given a class of competing models and new data, how one can improve the overall model performance. In general, there exist two solutions for this problem, namely model selection and model combination. Model selection is to select a single best model...

This paper is intended to solve a central problem in recently developed feature-based model combination method (Xu and Golay, 2005), that is, how to choose candidate models. Through our analysis, we first conclude that the efficiency of model combination highly depends on the choice of candidate...

Model selection has become a central task in science. The classical model selection such as variable selection in multiple linear regression delivers interpretable models, but its instability is also well known. On the other hand, some shrinkage estimators enjoy the property of stability. To...

This paper addresses an important issue in model combination, that is, model locality. Since usually a global linear model is unable to reflect nonlinearity and to characterize local features, especially in a complex system, we propose a mixture of local feature models in order to overcome these...

In this paper, we develop a new asymptotic theory of the long run variance estimator obtained by fitting a vector autoregressive model to the transformed moment processes in a GMM framework. In contrast to the conventional asymptotics where the VAR lag order p goes to infinity but at a slower...