This paper provides a novel approach to forecasting time series subject to discrete structural breaks. We propose a Bayesian estimation and prediction procedure that allows for the possibility of new breaks over the forecast horizon, taking account of the size and duration of past breaks (if...

We develop novel forecasting methods for panel data with heterogeneous parameters and examine them together with existing approaches. We conduct a systematic comparison of their predictive accuracy in settings with different cross-sectional (N) and time (T) dimensions and varying degrees of...

As skills of labor-market entrants are usually not directly observed by employers, individuals acquire skill signals. To study which signals are valued by employers, we simultaneously and independently randomize a broad range of skill signals on pairs of resumes of fictitious applicants among...