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Title: Theory and Application of Large Covariance Matrix Estimation in Panel Data Models


High dimensional covariance matrix estimation has seen its wide applications in panel data models and factor analysis. While the sparsity assumption on the covariance matrix directly might be restrictive, it is more reasonable to be satisfied when common factors are controlled first. This so-called "conditional sparsity (given factors)" assumption enables us to estimate various covariance matrices with good rate of convergence. Some applications in portfolio allocation are also presented.