This paper addresses the estimation of the nonparametric conditional moment restricted model that involves an infinite dimensional parameter g0. We estimate it in a quasi-Bayesian way based on the limited information likelihood, and investigate the impact of three types of priors on the...

We study the estimation of a high dimensional approximate factor model in the presence of both cross sectional dependence and heteroskedasticity. The classical method of principal components analysis (PCA) does not efficiently estimate the factor loadings or common factors because it essentially...

We propose a novel two-regime regression model where the switching between the regimes is driven by a vector of possibly unobservable factors. When the factors are latent, we estimate them by the principal component analysis of a much larger panel data set. Our approach enriches conventional...

We consider estimation of a dynamic distribution regression panel data model with heterogeneous coefficients across units. The objects of interest are functionals of these coefficients including linear projections on unit level covariates. We also consider predicted actual and stationary...

The variance covariance matrix plays a central role in the inferential theories of high dimensional factor models in finance and economics. Popular regularization methods of directly exploiting sparsity are not directly applicable to many financial problems. Classical methods of estimating the...

This paper presents a study of the large-sample behavior of the posterior distribution of a structural parameter which is partially identified by moment inequalities. The posterior density is derived based on the limited information likelihood. The posterior distribution converges to zero...

This paper deals with the estimation of a high-dimensional covariance with a conditional sparsity structure and fast-diverging eigenvalues. By assuming sparse error covariance matrix in an approximate factor model, we allow for the presence of some cross-sectional correlation even after taking...

This paper introduces a Projected Principal Component Analysis (Projected-PCA), which is based on the projection of the data matrix onto a given linear space before performing the principal component analysis. When it applies to high-dimensional factor analysis, the projection removes...

While applications of big data analytics have brought many new opportunities to economic research, with datasets containing tens of millions of observations, making usual econometric inferences based on extreme estimators would require huge computing powers and memories that are often not...