Correlation matrices are ubiquitous in finance. Some key applications include portfolio construction, risk management, and factor/style analysis. Correlation matrices are usually estimated from historical empirical observations or derived from historically estimated factors. It is widely...

We propose a simple and fast fixed-point iteration algorithm FiPIt to obtain the global, non-linear solution of macro models with two endogenous state variables and occasionally binding constraints. This method uses fixed-point iteration on Euler equations to avoid solving two simultaneous...

Cross-sector labor reallocation is associated with costs at the micro level ranging from the costs of geographical relocation and skill change/adaptation to unemployment. We show that monotonous reallocation paths minimize the aggregate reallocation costs in the three-sector framework (relating...

Matrices, their products, linear systems, and the underlying geometric ideas are presented in an intuitive and practical way for economics students and other students of the social sciences. Python Jupyter notebooks are used to present examples that enforce the geometric ideas.

Under rotation-equivariant decision theory, sample covariance matrix eigenvalues can be optimally shrunk by recombining sample eigenvectors with a (potentially nonlinear) function of the unobservable population covariance matrix. The optimal shape of this function reflects the loss/risk that is...

This paper constructs a new estimator for large covariance matrices by drawing a bridge between the classic Stein (1975) estimator in finite samples and recent progress under large-dimensional asymptotics. Our formula is quadratic: it has two shrinkage targets weighted by quadratic functions of...