Analysis Of Panel Data Based On Grouped Models

Panel data have been paid great attention by many scholars because of the unique data structure.Panel data,also known as longitudinal data,are the combination of cross-section data and time series data.Panel data are two-dimensional data obtained by repeated observation of each observed individual at different time periods.Panel data can provide not only independent information between individuals,but also continuous information in time.The advantage of panel data is that the repeated observations in one dimension can be used to study problems in another dimension.The classical panel data models are the static panel data models with individual effects and the dynamic panel data models with individual effects.Many scholars have put a lot of energy into these two models and have obtained rich research results.The main focus of this phase is on how to eliminate individual effects and estimate the parameters of interest in the transformed models.After the 21 st century,scholars have extended their original concerns to how to make inferences about individual characteristics.Based on the research of scholars,this paper has the following work.Firstly,in the classical dynamic panel data models,the generalized method of moments(GMM)is the most widely-used estimation method.However,the GMM estimator might suffer from a weak instruments problem when the autoregressive coefficient is near unity.To solve this problem,This paper proposes two new estimators of instrumental variables,and theoretically proves that the two methods do not have the problem of weak instrumental variables.The improvement of classical methods has always been an important content of panel data researchSecondly,this paper presents a panel data model with grouped individual effects and a two-stage estimation method.In the first stage,we estimate the regression coefficients.Then we use K-means algorithm to estimate the grouping and group effects in the second stage.Compared with the GFE method,the two-stage method is not affected by the differences between groups when estimating regression coefficients,and the two methods have the same good performance in estimating grouping and the group effects.Thirdly,this paper puts forward panel data models in which the regression coefficients change over time and individuals.Specifically,the regression coefficients have unobserved break points in time and are divided into several groups over the cross section.This kind of models makes full use of the advantages of panel data,so that the models can be described in more detail and the interpretation ability can be greatly strengthened.We propose a simple but very effective estimation method and it is sensitive to break.This paper makes significant research in two aspects.On the one hand,new estimation methods are proposed in the classical analysis,which effectively overcome the problem of weak instrumental variables.On the other hand,new models and estimation methods for inferring individual characteristics are proposed,which are important breakthroughs.