This paper introduces a new method for estimating variance matrices. Starting from the orthogonal decomposition of the sample variance matrix, we exploit the fact that orthogonal matrices are never ill-conditioned and therefore focus on improving the estimation of the eigenvalues. We estimate...

Statistical inferences for sample correlation matrices are important in high dimensional data analysis. Motivated by this, this paper establishes a new central limit theorem (CLT) for a linear spectral statistic (LSS) of high dimensional sample correlation matrices for the case where the...

Suppose a group of individuals must decide whether to undertake a public project. The private commodity space, from which are also drawn the inputs for the public good, exhibits the Riesz decomposition property. We give a sufficient condition for the existence of a feasible provision of the...

This paper aims to illustrate how weight matrices that are needed to construct foreign variable vectors in Global Vector Autoregressive (GVAR) models can be estimated jointly with the GVAR's parameters. An application to real GDP and consumption expenditure price inflation as well as a...

Slepian and Sudakov-Fernique type inequalities, which com- pare expectations of maxima of Gaussian random vectors under certain restrictions on the covariance matrices, play an important role in probability theory, especially in empirical process and extreme value theories. Here we give explicit...

Many economic models (e.g., computable general equilibrium models, econometric input–output models) revolve around a matrix of technical coefficients. However, these matrices can be estimated only once every 5 years as long as they are calculated from input–output tables and these are only...

When analyzing economic aspects at regional levels, the spatial dimension of the data plays a crucial role. This is the case as spatial data and models are often characterized by two spatial effects, namely spatial dependence and spatial heterogeneity. The field of spatial econometrics is a...

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 happens frequently, the sample covariance matrix is known to perform poorly and may suffer from ill-conditioning. There already...