Research On The Problem Of Missing Variables Under A Special Structure Of Panel Data

The models established by panel data are also diverse,and regression models are usually used to establish the relationship between the variables of interest(response variables)and explanatory variables,so as to explain the changes in the values of the variables of interest.However,in the analysis of many practical problems,the explanatory variables that are directly related to the response variable are difficult to obtain due to various practical reasons,and there is a problem of variable loss.If the influence of the omitted variable is ignored in the modeling process,it will be The estimation of model parameters is biased.In addition,in many practical problems,at a fixed point in time,each cross-sectional unit usually has multiple observations,and the number of observations varies with time and cross-sectional unit,which we call special structure panel data.This article considers the omission effect caused by the omission of variables in panel data analysis.Missing variables often appear in many daily applications,usually because the data is unavailable or unobserved.In this article,we propose a panel data model with a grouping intercept for panel data with a special structure,which enables us to consider the impact of missing variables.Especially for individual specific missing variables,in this case,it has unobservable The commonly used panel data model for interaction is invalidated.Empirical studies have shown that because the new model can explain the omission effects of any type of missing variables,the model proposed in this article is more effective than the existing omission variable models.