High-Dimensional Time-Varying Coefficient Estimation

Donggyu Kim ()
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Donggyu Kim: Department of Economics, University of California Riverside

No 202416, Working Papers from University of California at Riverside, Department of Economics

Abstract: In this paper, we develop a novel high-dimensional time-varying coefficient estimation method, based on high-dimensional Itô diffusion processes. To account for high-dimensional time-varying coefficients, we first estimate local (or instantaneous) coefficients using a time localized Dantzig selection scheme under a sparsity condition, which results in biased local coefficient estimators due to the regularization. To handle the bias, we propose a debiasing scheme, which provides well-performing unbiased local coefficient estimators. With the unbiased local coefficient estimators, we estimate the integrated coefficient, and to further account for the sparsity of the coefficient process, we apply thresholding schemes. We call this Thresholding dEbiased Dantzig (TED). We establish asymptotic properties of the proposed TED estimator. In the empirical analysis, we apply the TED procedure to analyzing high-dimensional factor models using high-frequency data.

Pages: 35 Pages
Date: 2024-12
New Economics Papers: this item is included in nep-ecm and nep-ets
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