This paper develops new model selection methods for forecasting panel data using a set of least squares (LS) vector autoregressions. Model selection is based on minimizing the estimated quadratic forecast risk among candidate models. We provide conditions under which the selection criterion is...

This paper evaluates the performance of a few newly proposed on-line forecast combination algorithms, and compares them with some of the existing ones including the simple average and that of Bates and Granger (1969). We derive asymptotic results for the new algorithms that justify certain...

This paper introduces structured machine learning regressions for prediction and nowcasting with panel data consisting of series sampled at different frequencies. Motivated by the empirical problem of predicting corporate earnings for a large cross-section of firms with macroeconomic, financial,...

This paper promotes the use of panel data in nowcasting. We shift the existing focus of the literature, which has almost exclusively used time series models to nowcast national aggregate variables like gross domestic product (GDP). We propose a mixed-frequency panel VAR model and a...

We propose a new forecast combination method for panel data vector autoregressions that permit limited forms of parameterized heterogeneity (including fixed effects or incidental trends). Models are fitted using bias-corrected least squares in order to attenuate the effects of small sample bias...

We develop a new set of model selection methods for direct multistep forecasting of panel data vector autoregressive processes. Model selection is based on minimizing the estimated multistep quadratic forecast risk among candidate models. In order to attenuate the small sample bias of the least...

This paper develops new methods for testing equal predictive accuracy in panels of forecasts that exploit information in the time series and cross-sectional dimensions of the data. Using a common factor setup, we establish conditions on cross-sectional dependencies in forecast errors which allow...

In this paper, we exploit micro data from the ECB Survey of Professional Forecasters (SPF) to examine the link between the characteristics of macroeconomic density forecasts (such as their location, spread, skewness and tail risk) and density forecast performance. Controlling for the effects of...