A wavelet method for panel models with jump discontinuities in the parameters

O. Bada, A. Kneip, D. Liebl, T. Mensinger, J. Gualtieri and Robin Sickles

Journal of Econometrics, 2022, vol. 226, issue 2, 399-422

Abstract: While a substantial literature on structural break change point analysis exists for univariate time series, research on large panel data models has not been as extensive. In this paper, a novel method for estimating panel models with multiple structural changes is proposed. The breaks are allowed to occur at unknown points in time and may affect the multivariate slope parameters individually. Our method adapts Haar wavelets to the structure of the observed variables in order to detect the change points of the parameters consistently. We also develop methods to address endogenous regressors within our modeling framework. The asymptotic property of our estimator is established. In our application, we examine the impact of algorithmic trading on standard measures of market quality such as liquidity and volatility over a time period that covers the financial meltdown that began in 2007. We are able to detect jumps in regression slope parameters automatically without using ad-hoc subsample selection criteria.

Keywords: Haar wavelets; Adaptive lasso; Panel data; Structural change; Penalized IV; Algorithmic trading; Market efficiency (search for similar items in EconPapers)
JEL-codes: C13 C14 C23 C33 C51 G14 (search for similar items in EconPapers)
Date: 2022
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Working Paper: A Wavelet Method for Panel Models with Jump Discontinuities in the Parameters (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:226:y:2022:i:2:p:399-422

DOI: 10.1016/j.jeconom.2021.09.006

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