A Wavelet Method for Panel Models with Jump Discontinuities in the Parameters

TL;DR

This paper introduces a wavelet-based method for detecting multiple structural changes in large panel data models, allowing for unknown change points and individual parameter shifts, with applications to financial market analysis.

Contribution

It develops a novel Haar wavelet approach for consistent change point detection in panel models with endogenous regressors, addressing a gap in existing structural break analysis.

Findings

Successfully detects change points in simulated data

Identifies parameter jumps in financial market data

Provides asymptotic properties of the estimator

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.