This paper concerns estimating parameters in a high-dimensional dynamic factor model by the method of maximum likelihood. To accommodate missing data in the analysis, we propose a new model representation for the dynamic factor model. It allows the Kalman filter and related smoothing methods to...

This study presents an approach to apply the maximum likelihood estimation (MLE) method to estimate the parameters in quantitative spatial economic models. The proposed method can be applied to any model in which the unique values of the error terms can be recovered from the observed data on the...

Quantile factor models (QFM) represent a new class of factor models for high-dimensional panel data. Unlike approximate factor models (AFM), which only extract mean factors, QFM also allow unobserved factors to shift other relevant parts of the distributions of observables. We propose a quantile...

Quantile factor models (QFM) represent a new class of factor models for high-dimensional panel data. Unlike approximate factor models (AFM), which only extract mean factors, QFM also allow unobserved factors to shift other relevant parts of the distributions of observables. We propose a quantile...

Polynomial factor models (henceforth, PFM) represent a new class of factor models for high-dimensional panel data. We develop several econometric theories for factor models of latent factor interactions. Unlike approximate factor models (AFM), which are based on linear combinations of observed...

In this paper we study doubly robust estimators of various average treatment effects under unconfoundedness. We unify and extend much of the recent literature by providing a very general identification result which covers binary and multi-valued treatments; unnormalized and normalized weighting;...

In this study, I investigate the necessary condition for the consistency of the maximum likelihood estimator (MLE) of spatial models with a spatial moving average process in the disturbance term. I show that the MLE of spatial autoregressive and spatial moving average parameters is generally...

To analyze data obtained by non-random sampling in the presence of cross-sectional dependence, estimation of a sample selection model with a spatial lag of a latent dependent variable or a spatial error in both the selection and outcome equations is considered. Since there is no estimation...

This paper evaluates the performance of alternative estimation methods for multiplicative and log models with heteroskedasticity. Contrary to Santos Silva and Tenreyro (2006), the results of a simulation study indicate that the Pseudo Poisson Maximum Likelihood estimator (PPML) is not always the...

A system of regression equations (SURE) for analyzing panel data with random heterogeneity in intercepts and coefficients, and unbalanced panel data is considered. A Maximum Likelihood (ML) procedure for joint estimation of all parameters is described. Since its implementation for numerical...