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

This chapter provides an overview of and user's guide to dynamic factor models (DFMs),
their estimation, and their uses in empirical macroeconomics. It also surveys recent …

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 …

The evaluation of macroeconomic policy decisions has traditionally relied on the formulation
of a specific economic model. In this work, we show that two statistics are sufficient to detect …

This lively book lays out a methodology of confidence distributions and puts them through
their paces. Among other merits they lead to optimal combinations of confidence from …

Research into predictive accuracy testing remains at the forefront of the forecasting field.
One reason for this is that rankings of predictive accuracy across alternative models, which …

We study the asymptotic properties of the Adaptive LASSO (adaLASSO) in sparse, high-
dimensional, linear time-series models. The adaLASSO is a one-step implementation of the …

This paper provides an empirical evaluation of the US aggregate stock market predictability
based on a new technical analysis index that eliminates the idiosyncratic noise component …

We investigate the information content of implied volatility forecasts for stock index return
volatility. Using different autoregressive models, we examine whether implied volatility …

This article provides guidance on how to evaluate and improve the forecasting ability of
models in the presence of instabilities, which are widespread in economic time series …