Classical regression analysis uses partial coefficients to measure the influences of some variables (regressors) on another variable (regressand). However, a descriptive point of view shows that these coefficients are very bad measures of influence. Their interpretation as an average change of...

Explained variance (R^2) is a familiar summary of the fit of a linear regression and has been generalized in various ways to multilevel (hierarchical) models. The multilevel models we consider in this paper are characterized by hierarchical data structures in which individuals are grouped into...

Two-step estimation with large panel data sets generally involves estimating vectors of individual-specific coefficients in a first-stage. In a second-stage estimation a vector of estimated coefficients is used as the dependent variable. Potential problems of heteroskedasticity in the second...

The optimal minimum distance (OMD) estimator for models of covariance structures is asymptotically efficient but has much worse finite-sample properties than does the equally-weighted minimum distance (EWMD) estimator. This paper shows how the bootstrap can be used to improve the finite-sample...

This paper is concerned with the problems of posterior simulation and model choice for Poisson panel data models with multiple random effects. Efficient algorithms based on Markov Chain Monte Carlo methods for sampling the posterior distribution are developed. A new parameterization of the...

I derive a generalized version of the fundamental law of active management under some weak conditions. I show that the original fundamental law of Grinold and various extensions are special cases of the result presented in this paper. I also show that cross-sectional ICs are usually different...

This paper studies the importance of accounting for term structure maturity clusters while estimating latent factors, for the purpose of forecasting yield curves. The maturity clusters are identified using a hierarchical clustering algorithm. We then propose a new block dynamic Nelson-Siegel...

This paper describes a methodology to estimate the coefficients, to test specification hypotheses and to conduct policy exercises in multi-country VAR models with cross unit interdependencies, unit specific dynamics and time variations in the coefficients. The framework of analysis is Bayesian:...

We estimate the demand for health insurance in the California Affordable Care Act marketplace (Covered California) without using parametric assumptions about the unobserved components of utility. To do this, we develop a computational method for constructing sharp identified sets in a...

In this paper we introduce a test for the normality assumption in the sample selection model. The test is based on a generalization of a semi-nonparametric maximum likelihood method. In this estimation method, the distribution of the error terms is approximated by a Hermite series, with...