Estimation of common breaks in linear panel data models via screening and ranking algorithm

Area of Science:

• Econometrics
• Time Series Analysis
• Economic Modeling

Background:

• Panel data models are crucial for analyzing economic phenomena across multiple entities over time.
• Identifying structural breaks in these models is essential for accurate policy evaluation and forecasting.
• Existing methods may face challenges in accurately estimating common break points in both static and dynamic panel data.

Purpose of the Study:

• To develop and validate a novel screening and ranking algorithm for estimating common break points in linear panel data models.
• To assess the performance of the proposed algorithm in static and dynamic panel data settings.
• To apply the algorithm to a real-world economic dataset to identify significant structural changes.

Main Methods:

• Estimation of regression coefficients using covariance estimation for static models and generalized method of moments for dynamic models.
• A multi-stage screening and ranking algorithm involving local statistics, thresholding rules, and information criteria to identify break points.
• Monte Carlo simulations to evaluate the finite sample performance of the proposed methods.

Main Results:

• The proposed screening and ranking algorithm demonstrates good performance in finite samples across various panel data model specifications.
• The algorithm successfully identified a significant break point in the relationship between rural consumption demand and China's economic growth.
• The application highlights the practical utility of the method in uncovering structural shifts in economic relationships.

Conclusions:

• The developed screening and ranking algorithm provides a robust and effective approach for estimating common break points in linear panel data models.
• The method is reliable in both static and dynamic settings, offering a valuable tool for econometricians.
• The study underscores the importance of considering structural breaks when analyzing the drivers of economic growth, as exemplified by the China case study.