This paper considers the problem of identification and estimation in panel-data sample-selection models with a binary selection rule when the latent equations contain possibly predetermined variables, lags of the dependent variables, and unobserved individual effects. The selection equation...

In this paper we apply a learning model from machine learning, to a human trading crowd to understand why the no trade theorem was rejected. Our results reveal that trading volume in a continuous double auction market is associated with inverse learning curves. Inverse learning results from...

We develop a method for identifying and quantifying the fiscal channels that help finance government spending shocks. We define fiscal shocks as surprises in defense spending and show that they are more precisely identified when defense stock data are used in addition to aggregate macroeconomic...