An Example of Panel Data Analysis (Parallel Data Analysis) Using MATLAB

In this article, we introduce how to perform panel data analysis using MATLAB while providing a practical example of parallel data analysis. We begin by discussing the definition and purpose of panel data analysis, along with the rationale for selecting MATLAB as our computational tool - emphasizing its statistical toolbox functions like panel and fitlme for linear mixed-effects models. Next, we detail data preparation procedures including data cleaning workflows using MATLAB's table data type and variable selection techniques implemented through correlation analysis and stepwise regression algorithms. We then explore panel data models (fixed effects, random effects) and common analytical methods, providing technical insights on implementing Hausman tests and Lagrange multiplier tests programmatically. The article includes practical techniques such as handling missing data with fillmissing functions and optimizing computation performance through MATLAB's parallel computing toolbox. Finally, we present a concrete example demonstrating parallel data analysis techniques in MATLAB, showcasing how to implement distributed computing for large panel datasets using parfor loops and parallelized estimation algorithms. This comprehensive guide aims to help researchers better understand and apply panel data analysis methodologies through hands-on MATLAB implementation.