Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/247185 
Year of Publication: 
2020
Series/Report no.: 
Working Paper No. 916
Publisher: 
Queen Mary University of London, School of Economics and Finance, London
Abstract: 
Unemployment, firm Dynamics, and the Business CyclTime variation is a fundamental problem in statistical and econometric analysis of macroeconomic and financial data. Recently there has been considerable focus on developing econometric modelling that enables stochastic structural change in model parameters and on model estimation by Bayesian or non-parametric kernel methods. In the context of the estimation of covariance matrices of large dimensional panels, such data requires taking into account time variation, possible dependence and heavy-tailed distributions. In this paper we introduce a non-parametric version of regularisation techniques for sparse large covariance matrices, developed by Bickel and Levina (2008) and others. We focus on the robustness of such a procedure to time variation, dependence and heavy-tailedness of distributions. The paper includes a set of results on Bernstein type inequalities for dependent unbounded variables which are expected to be applicable in econometric analysis beyond estimation of large covariance matrices. We discuss the utility of the robust thresholding method, comparing it with other estimators in simulations and an empirical application on the design of minimum variance portfolios.
Subjects: 
covariance matrix estimation
large dataset
regularization
thresholding
shrinkage
exponential inequalities
minimum variance portfolio
JEL: 
C13
C22
C51
Document Type: 
Working Paper

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