We examine the driving forces behind farmers’ decisions to adapt to climate change, and the impact of adaptation on farmers’ food production. We investigate whether there are differences in the food production functions of farm households that adapted and those that did not adapt. We...

An asymptotic theory is developed for nonparametric and semiparametric series estimation under general cross-sectional dependence and heterogeneity. A uniform rate of consistency, asymptotic normality, and sufficient conditions for convergence, are established, and a data-driven studentization...

In the estimation of parametric models for stationary spatial or spatio-temporal data on a d-dimensional lattice, for d ≥ 2, the achievement of asymptotic efficiency under Gaussianity, and asymptotic normality more generally, with standard convergence rate, faces two obstacles. One is the...

This paper describes a method for estimating panels by imposing a factor structure on the residuals. The method allows SUR estimation of panel models by providing a full-rank estimator of the system covariance matrix when the usual estimate is rank-deficient. We charactersie completely the...

Using the restrictions implied by the heteroskedasticity of stock returns, we identify four factors in the U.S. industry returns. The first correlates highly with the market portfolio; the second is a portfolio of stocks that produce investment goods minus stocks that produce consumption goods;...

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...

This paper investigates the effect of minimum wages on employment using a panel of US state-based data. We estimate a minimalist dynamic version of the specification implied by neo-classical theory. We find statistically and economically significant effects of minimum wages on youth employment....

Pairwise maximum likelihood (PML) estimation method is developed for factor analysis models with ordinal data and fitted both in an exploratory and confirmatory set-up. The performance of the method is studied via simulations and comparisons with full information maximum likelihood (FIML) and...

GARCH models are commonly used as latent processes in econometrics, financial economics and macroeconomics. Yet no exact likelihood analysis of these models has been provided so far. In this paper we outline the issues and suggest a Markov chain Monte Carlo algorithm which allows the calculation...

Epidemics are often modeled using non-linear dynamical systems observed through partial and noisy data. In this paper, we consider stochastic extensions in order to capture unknown influences (changing behaviors, public interventions, seasonal effects, etc.). These models assign diffusion...