The objective of the paper is that of constructing finite Gaussian mixture approximations to analytically intractable density kernels. The proposed method is adaptive in that terms are added one at the time and the mixture is fully re-optimized at each step using a distance measure that...

PRELIMINARY DRAFT We discuss maximum likelihood (ML) analysis for panel count data models, in which the observed counts are linked via a measurement density to a latent Gaussian process with spatial as well as temporal dynamics and random effects. For likelihood evaluation requiring...

We analyze the determinants of effective legal institutions (legality) using data from forty-nine countries. We show that the way the law was initially transplanted and received is a more important determinant than the supply of law from a particular legal family. Countries that have developed...

We analyze the determinants of effective legal institutions (legality) using data from 49 countries. We show that the way the law was initially transplanted and received is a more important determinant than the supply of law from a particular legal family. Countries that have developed legal...

We analyze the determinants of effective legal institutions (legality) using data from 49 countries. We show<p> that the way the law was initially transplanted and received is a more important determinant than the supply<p> of law from a particular legal family. Countries that have developed legal...</p></p>

The paper describes a simple, generic and yet highly accurate Efficient Importance Sampling (EIS) Monte Carlo (MC) procedure for the evaluation of high-dimensional numerical integrals. EIS is based upon a sequence of auxiliary weighted regressions which actually are linear under appropriate...

In this paper, Efficient Importance Sampling (EIS) is used to perform a classical and Bayesian analysis of univariate and multivariate Stochastic Volatility (SV) models for financial return series. EIS provides a highly generic and very accurate procedure for the Monte Carlo (MC) evaluation of...

In this paper, we perform Bayesian analysis of a panel probit model with unobserved individual heterogeneity and serially correlated errors. We augment the data with latent variables and sample the unobserved heterogeneity component as one Gibbs block per individual using a flexible piecewise...