We use panel probit models with unobserved heterogeneity and serially correlated errors in order to analyze the determinants and the dynamics of current-account reversals for a panel of developing and emerging countries. The likelihood evaluation of these models requires high-dimensional...

The volatility of growth in U.S. real GDP declined dramatically in the mid-1980s. Viewed through the lens of linear autoregressive models, this phenomenon appears to be the result of a structural break in the innovation process that drives GDP fluctuations. We present an alternative model that...

We estimate stochastic volatility leverage models for a panel of stock returns for 24 S&P 500 firms from six industries. News are measured as differences between daily return and a monthly moving average of past returns. We estimate the models by maximum likelihood using an Efficient Importance...

In this paper we consider ML estimation for a broad class of parameter-driven models for discrete dependent variables with spatial correlation. Under this class of models, which includes spatial discrete choice models, spatial Tobit models and spatial count data models, the dependent variable is...