This paper deals with estimation of high-dimensional covariance with a conditional sparsity structure, which is the composition of a low-rank matrix plus a sparse matrix. By assuming sparse error covariance matrix in a multi-factor model, we allow the presence of the cross-sectional correlation...

Estimating and assessing the risk of a large portfolio is an important topic in financial econometrics and risk management. The risk is often estimated by a substitution of a good estimator of the volatility matrix. However, the accuracy of such a risk estimator for large portfolios is largely...

Most papers on high-dimensional statistics are based on the assumption that none of the regressors are correlated with the regression error, namely, they are exogenous. Yet, endogeneity arises easily in high-dimensional regression due to a large pool of regressors and this causes the...

Estimating and assessing the risk of a large portfolio is an important topic in financial econometrics and risk management. The risk is often estimated by a substitution of a good estimator of the volatility matrix. However, the accuracy of such a risk estimator for large portfolios is largely...

Research has paid scant attention to reparative behavior to compensate for unintended wrongdoing or to the role of emotions in doing the right thing. We propose a new approach to investigating reparative behavior by looking at moral emotions and psychological proximity. In this study, we compare...

Weak capacity to enforce regulations and sanction violators, and an emphasis on economic growth in developing countries has led to concerns about worsening environmental conditions and the potential for these countries becoming pollution havens for multinational corporations. International...

As organizations globalize, culturally based variation in the ways employees conceptualize their relationships with their employers increases in importance. In the competition for talent, organizations must understand employee preferences and expectations regarding their treatment by the firm....

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...

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...