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.