Estimation of High-Dimensional Dynamic Conditional Precision Matrices with an Application to Forecast Combination

Tae Hwy Lee, Millie Yi Mao () and Aman Ullah
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Millie Yi Mao: Azusa Pacific University

No 202012, Working Papers from University of California at Riverside, Department of Economics

Abstract: The estimation of a large covariance matrix is challenging when the dimension p is large relative to the sample size n. Common approaches to deal with the challenge have been based on thresholding or shrinkage methods in estimating covariance matrices. However, in many applications (e.g., regression, forecast combination, portfolio selection), what we need is not the covariance matrix but its inverse (the precision matrix). In this paper we introduce a method of estimating the high-dimensional "dynamic conditional precision" (DCP) matrices. The proposed DCP algorithm is based on the estimator of a large unconditional precision matrix by Fan and Lv (2016) to deal with the high-dimension and the dynamic conditional correlation (DCC) model by Engle (2002) to embed a dynamic structure to the conditional precision matrix. The simulation results show that the DCP method performs substantially better than the methods of estimating covariance matrices based on thresholding or shrinkage methods. Finally, inspired by Hsiao and Wan (2014), we examine the "forecast combination puzzle" using the DCP, thresholding, and shrinkage methods.

Keywords: High-dimensional conditional precision matrix; ISEE; DCP; Forecast combination puzzle. (search for similar items in EconPapers)
JEL-codes: C3 C4 C5 (search for similar items in EconPapers)
Pages: 23 Pages
Date: 2020-06
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-ore
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Forthcoming in Econometric Reviews

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Journal Article: Estimation of high-dimensional dynamic conditional precision matrices with an application to forecast combination (2021)
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