We contrast two approaches to the prediction of latent variables in the model of factor analysis. The likelihood statistic is a sufficient statistic for the unobservables when sampling arises from the exponential family of distributions. Linear predictors, on the other hand, can be obtained as...

In 1997 Rwanda introduced a re-settlement policy for refugees displaced during previous conflicts. We exploit geographic variation in the speed of implementation of this policy to investigate the impact of conflict-induced displacement and the re-settlement policy on household agricultural...

This paper investigates a unique feature of the English educational system to estimate the causal effect of compulsory schooling on labour market outcomes. We examine school leaving rules that allow for discrete variation in exit dates by date of birth within school cohorts. This natural...

We consider cross-sectional data that exhibit no spatial correla- tion, but are feared to be spatially dependent. We demonstrate that a spatial version of the stochastic volatility model of nancial econometrics, entailing a form of spatial autoregression, can explain such behaviour. The...

We utilize the Becker-DeGroot-Marschak (1964) mechanism to estimate the willingness to pay for clean drinking water technology in northern Ghana. The BDM mechanism has attractive properties for empirical research, allowing us to directly estimate demand, compute heterogeneous treatment effects,...

In this paper, we obtain the density function of the single barrier one-sided Parisian stopping time. The problem reduces to that of solving a Volterra integral equation of the first kind, where a recursive solution is consequently obtained. The advantage of this new method as compared to that...

We propose to model multivariate volatility processes on the basis of the newly defined conditionally uncorrelated components (CUCs). This model represents a parsimonious representation for matrix-valued processes. It is flexible in the sense that each CUC may be fitted separately with any...

We suggest two improved methods for conditional density estimation. The rst is based on locally tting a log-linear model, and is in the spirit of recent work on locally parametric techniques in density estimation. The second method is a constrained local polynomial estimator. Both methods always...

To measure income inequality with right-censored (top-coded) data, we propose multiple-imputation methods for estimation and inference. Censored observations are multiply imputed using draws from a flexible parametric model fitted to the censored distribution, yielding a partially synthetic data...