Many statistical applications require an estimate of a covariance matrix and/or its inverse.When the matrix dimension is large compared to the sample size, which happensfrequently, the sample covariance matrix is known to perform poorly and may suffer fromill-conditioning. There already exists...

Democracy is defined by two core tenets: voice and pluralism. Within these constraints, a wide variety of regime types can be designed. We show that the only new, untested form of democracy is when every citizen is governed by the political party of his/her choice. Multiple full-edged...

In reaction to the monetary turmoil created by the financial crisis of September 2008, both legislative and constitutional reforms have been proposed in different Countries to introduce Commodity Money alongside existing National Fiat Currency. A thorough evaluation of the Economic consequences...

Covariance matrix estimation and principal component analysis (PCA) are two cornerstones of multivariate analysis. Classic textbook solutions perform poorly when the dimension of the data is of a magnitude similar to the sample size, or even larger. In such settings, there is a common remedy for...

This paper revisits the methodology of Stein (1975, 1986) for estimating a covariance matrix in the setting where the number of variables can be of the same magnitude as the sample size. Stein proposed to keep the eigenvectors of the sample covariance matrix but to shrink the eigenvalues. By...

We introduce a model of the economy as a social network. Two agents are linked to the extent that they transact with each other. This generates well-defined topological notions of location, neighborhood and closeness. We investigate the implications of our model for monetary economics. When a...

There has been a recent debate in the marketing literature concerning the possible mispricing of customer satisfaction. While earlier studies claim that portfolios with attractive out-of-sample properties can be formed by loading on stocks whose firms enjoy high customer satisfaction, later...

This paper introduces a new method for deriving covariance matrix estimators that are decision-theoretically optimal. The key is to employ large-dimensional asymptotics: the matrix dimension and the sample size go to infinity together, with their ratio converging to a finite, nonzero limit. As...

The mispricing of marketing performance indicators (such as brand equity, churn, and customer satisfaction) is an important element of arguments in favor of the financial value of marketing investments. Evidence for mispricing can be assessed by examining whether or not portfolios composed of...

Markowitz (1952) portfolio selection requires estimates of (i) the vector of expected returns and (ii) the covariance matrix of returns. Many proposals to address the first question exist already. This paper addresses the second question. We promote a new nonlinear shrinkage estimator of the...