High dimensional threshold model with a time-varying threshold based on Fourier approximation

Yang Lixiong ()
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Yang Lixiong: School of Management, Lanzhou University, Lanzhou, China

Studies in Nonlinear Dynamics & Econometrics, 2024, vol. 28, issue 1, 83-117

Abstract: This paper studies high-dimensional threshold models with a time-varying threshold approximated using a Fourier function. We develop a weighted LASSO estimator of regression coefficients as well as the threshold parameters. Our LASSO estimator can not only select covariates but also distinguish between linear and threshold models. We derive non-asymptotic oracle inequalities for the prediction risk, the l 1 and l ∞ bounds for regression coefficients, and provide an upper bound on the l 1 estimation error of the time-varying threshold estimator. The bounds can be translated easily into asymptotic consistency for prediction and estimation. We also establish the variable selection consistency and threshold detection consistency based on the l ∞ bounds. Through Monte Carlo simulations, we show that the thresholded LASSO works reasonably well in finite samples in terms of variable selection, and there is little harmness by the allowance for Fourier approximation in the estimation procedure even when there is no time-varying feature in the threshold. On the contrary, the estimation and variable selection are inconsistent when the threshold is time-varying but being misspecified as a constant. The model is illustrated with an empirical application to the famous debt-growth nexus.

Keywords: Fourier approximation; high dimensions; threshold model; time-varying threshold (search for similar items in EconPapers)
JEL-codes: C13 C51 C52 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1515/snde-2021-0047

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