  Estimating large covariance and precision matrices are fundamental in modern
multivariate analysis. The problems arise from statistical analysis of large
panel economics and finance data. The covariance matrix reveals marginal
correlations between variables, while the precision matrix encodes conditional
correlations between pairs of variables given the remaining variables. In this
paper, we provide a selective review of several recent developments on
estimating large covariance and precision matrices. We focus on two general
approaches: rank based method and factor model based method. Theories and
applications of both approaches are presented. These methods are expected to be
widely applicable to analysis of economic and financial data.
