The COV= option must be specified to compute an approximate covariance matrix for the parameter estimates under asymptotic theory for least-squares, maximum-likelihood, or Bayesian estimation, with or ...

Covariance matrix estimation, crucial for multivariate inference, faces significant challenges when the number of variables rivals or exceeds the sample size.

John C. Driscoll, Aart C. Kraay, Consistent Covariance Matrix Estimation with Spatially Dependent Panel Data, The Review of Economics and Statistics, Vol. 80, No. 4 ...

This paper presents a parameter covariance matrix estimator which is consistent even when the disturbances of a linear regression model are heteroskedastic. This estimator does not depend on a formal ...

This paper proposes a novel shrinkage estimator for high-dimensional covariance matrices by extending the Oracle Approximating Shrinkage (OAS) of Chen et al. (2009) to target the diagonal elements of ...

Sparse arrays offer economic advantages by reducing the number of antennas. However, directly utilizing the covariance matrix of sparse array signals for wideband beamforming may lead to the emergence ...

We compute cosine similarity measures to compare risk factor reports and use them in linear regressions to estimate the covariance matrix of stock returns. Our estimators using risk report data ...