In ecology, the true causal structure for a given problem is often not known, and several
plausible models and thus model predictions exist. It has been claimed that using weighted …

This paper considers the model averaging prediction in a quasi-likelihood framework that
allows for parameter uncertainty and model misspecification. We propose an averaging …

Inflation forecasting is an important but difficult task. Here, we explore advances in machine
learning (ML) methods and the availability of new datasets to forecast US inflation. Despite …

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 …

Spatial econometrics relies on the spatial weights matrix to specify the cross-sectional
dependence; however, the candidate spatial weights matrices might not be unique. This …

This paper derives the limiting distributions of least squares averaging estimators for linear
regression models in a local asymptotic framework. We show that the averaging estimators …

In this paper we consider model averaging for quantile regressions (QR) when all models
under investigation are potentially misspecified and the number of parameters is diverging …

Many economic indicators including consumer confidence indices used to forecast volatility
or macroeconomic outcomes, are published with a considerable time lag. To obtain a …

This paper develops a frequentist model averaging method based on the leave-subject-out
cross-validation. This method is applicable not only to averaging longitudinal data models …

Structural changes often occur in economics and finance due to changes in preferences,
technologies, institutional arrangements, policies, crises, etc. Improving forecast accuracy of …