Much is written about the use of factors estimated by the method of principal components from large panels in linear regression models. In this paper, we provide an analysis for non-linear estimation and establish the conditions under which the estimated factors can be treated as though they...

We investigate a class of estimators for linear regression models where the dependent variable is subject to bid-ask censoring. Our estimation method is based on a definition of error that is zero when the predictor lies between the actual bid price and ask price, and linear outside this range....

This paper analyzes vector autoregressive models (VAR) with multiple structural changes. One distinct feature of this paper is the explicit consideration of structural changes in the variance-covariance matrix, in addition to changes in the autoregressive coefficients. The model is estimated by...