We study the identification and estimation of linear factor models under the assumptions that factors and errors are independent and that factors are not normally distributed. High-order moments are shown to yield full identification of the matrix of factor loadings if factor distributions are sufficiently skewed or kurtotic. We develop simple algorithms to estimate the matrix of factor loading...

This paper develops an asymptotic theory for a non-linear parametric co-integrating regression model. We establish a general framework for weak consistency that is easy to apply for various non-stationary time series, including partial sum of linear process and Harris recurrent Markov chain. We provide a limit distribution for the nonlinear least square estimator which significantly extends the...

This paper presents Genetic Matching, a method of multivariate matching, that uses an evolutionary search algorithm to determine the weight each covariate is given. Both propensity score matching and matching based on Mahalanobis distance are limiting cases of this method. The algorithm makes transparent certain issues that all matching methods must confront. We present simulation studies that ...

This paper examines asymptotic expansions for estimation errors expressed explicitly as functions of underlying random variables. Taylor series expansions are obtained from which "rst and second moment approximations are derived. While the expansions are essentially equivalent to the traditional Nagar type, the terms are expressed in a form which enables moment approximations to be obtained in ...

We complement existing inferential theory for panel factor models by deriving the asymptotics for the …rst di¤erences of the estimated factors and common components obtained from a non-stationary panel factor model. As an application, we propose an estimator for the long run variance of the common components. JEL Classi…cation: C13, C23. Keywords: Non-stationary panels, common factors, common c...

This paper considers ML estimation of a diffusion process observed discretely. Since the exact loglikelihood is generally not available, it must be approximated. We review the most efficient approaches in the literature, and point to some drawbacks. We propose to approximate the loglikelihood using the EIS strategy (Richard and Zhang, 1998), and detail its implementation for univariate homogene...

In empirical work on multivariate financial time series, it is common to postulate a Multivariate GARCH model. We show that the popular Gaussian quasi-maximum likelihood estimator of MGARCH models is very sensitive to outliers in the data. We propose to use robust M-estimators and provide asymptotic theory for M-estimators of MGARCH models. The Monte Carlo study and empirical application docume...

We consider a stochastic environment to study interactions among pollution growth, demographic changes, and economic growth. Drawing on the empirical findings of slow convergence patterns of pollution shocks (viz., with a long-memory), we build an analytical framework where stochastic environmental feedback effects on population changes are reflected upon aggregate economic growth. Long-memory ...

This paper provides estimators of discrete choice models, including binary, ordered, and multinomial response (choice) models. The estimators closely resemble ordinary and two stage least squares. The distribution of the model’s latent variable error is unknown and may be related to the regressors, e.g., the model could have errors that are heteroscedastic or correlated with regressors. The est...

When the overidentifying restrictions of the constant-effect linear instrumental variables model fail, common IV estimators converge to different probability limits. I characterize the estimands of two stage least squares, two step GMM, and limited information maximum likelihood as functions of the single-instrument estimands from the just-identified IV regressions which consider each instrumen...