Analysts often rely on methods that presume constant stochastic variance, even though its degree can differ markedly across experimental and field settings. This reliance can lead to misestimation of effect sizes or unjustified theoretical or behavioral inferences. Classic utility-based...

We examine the specification and interpretation of discrete-choice models used in behavioral theory testing, with a focus on separating “coefficient scale” from “error scale,” particularly over time. Numerous issues raised in the thoughtful commentaries of Louviere and Swait [Louviere,...

This paper examines volatility spillovers from mature to emerging stock markets and tests for changes in the transmission mechanism-contagion-during turbulences in mature markets. Tri-variate GARCH-BEKK models of returns in global (mature), regional, and local markets are estimated for 41...

The existing literature suggests a number of alternative methods to test for the presence of contagion during financial market crises. This paper reviews those methods and shows how they are related in a unified framework. A number of extensions are also suggested that allow for multivariate...

This paper develops a small open economy model where entrepreneurs partially finance investment using foreign currency denominated debt subject to a risk premium above and beyond international interest rates. We use Bayesian estimation techniques to evaluate the importance of balance sheet...

A stochastic volatility model where volatility was driven solely by a latent variable called news was estimated for three stock indices. A Markov chain Monte Carlo algorithm was used for estimating Bayesian parameters and filtering volatilities. Volatility persistence being close to one was...

Strategies aimed at poverty reduction need to identify factors that are strongly associated with poverty and that are amenable to modification by policy. This article uses household level data collected in 1994 to examine probable determinants of poverty status, employing both binomial and...

Operations reversal is a process design principle that involves switching two consecutive stages of the manufacturing process to improve process performance. In this paper we investigate conditions under which operations reversal can be used to reduce the variability---as measured by the...

The paper presents probability models to recover information on migration flows from incomplete data. Models are used to predict migration and to combine data from different sources. The parameters of the model are estimated from the data by the maximum likelihood method. If data are incomplete,...