Panel Data Models With Interactive Fixed Effects and Multiple Structural Breaks

Abstract

In this paper we consider estimation of common structural breaks in panel data models with interactive fixed effects which are unobservable. We introduce a penalized principal component (PPC) estimation procedure with an adaptive group fused LASSO to detect the multiple structural breaks in the models. Under some mild conditions, we show that with probability approaching one the proposed method can correctly determine the unknown number of breaks and consistently estimate the common break dates. Furthermore, we estimate the regression coefficients through the post-LASSO method and establish the asymptotic distribution theory for the resulting estimators. The developed methodology and theory are applicable to the case of dynamic panel data models. The Monte Carlo simulation results demonstrate that the proposed method works well in finite samples with low false detection probability when there is no structural break and high probability of correctly estimating the break numbers when the structural breaks exist. We finally apply our method to study the environmental Kuznets curve for 74 countries over 40 years and detect two breaks in the data.

Estimation of heterogeneous panels with structural breaks

“…Hsu and Lin (2012) examine the consistency properties of the change point estimators for nonstationary panels. More recently, Qian and Su (2014) and Li, Qian and Su (2014) study the estimation and inference of common breaks in panel data models with and without interactive …xed e¤ects using Lasso-type methods. In terms of detecting structural breaks in panels, some recent literature includes Horváth and Hušková The paper is organized as follows.…”

Common Breaks in Means for Cross‐Correlated Fixed‐T Panel Data

“…Except for these conditions, F t is completely unrestricted. This is in stark contrast to the large‐ T literature on break detection in the presence of common factors, where it is standard to assume that converges to a positive constant (see, e.g., Kim, ; Baltagi et al , ; Li et al , ), thereby ruling out unit root factors and factors with deterministic trends. Looking at the existing fixed‐ T literature, as alluded to in Section 1, Assumption 4 represents a very substantial improvement.…”

“…Indeed, while the number of time periods is limited and cannot be increased other than by the passage of time, statistical agencies keep publishing time‐series data for individuals, firms, countries, and regions. Thus, while T is usually quite small, N can potentially be very large (see De Wachter and Tzavalis, ; Horváth and Hušková, ; Li et al , ; Hidalgo and Schafgans, ; Peštová and Pešta, , for some examples of applications where N is relatively large).…”

An Integrated Panel Data Approach to Modelling Economic Growth

“…Assumption 5.1 is identical to Assumption iii of Li et al (2016) and Assumption A.1.v of Lu and Su (2016). Assumption 5.2 further imposes bounds on some parameters, and can be verified in exactly the same way as shown under Assumption 3.…”