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面板数据模型中逐系数结构突变的估计AI 解读

When estimating structural breaks, existing econometric methods adopt an approach in which either all parameters change simultaneously, or they remain the same. In this paper, we consider the estimation of panel data models when an unknown subset of coefficients is subject to breaks. The challenge lies in estimating the breaks for each coefficient. To tackle this, we propose a new estimator for panel data, the “Coefficient-by-Coefficient Lasso” break estimator. This…

固定时间维度下含随机交互效应与多重结构突变的面板模型估计AI 解读

In this article, we propose a new estimator of panel data models with random interactive effects and multiple structural breaks that is suitable when the number of time periods, T, is fixed and only the number of cross-sectional units, N, is large. This is done by viewing the determination of the breaks as a shrinkage problem, and to estimate both the regression coefficients, and the number of breaks and their locations by applying a version of the Lasso approach. We show…