First, the basic characteristics of panel data
Panel data has two dimensions: individual dimension and time dimension. Individual dimension represents the individual characteristics of the research object in a certain period, such as individuals, families, enterprises, etc. The time dimension represents the observation period, which can be year, quarter, month, etc. The basic characteristics of panel data include balance, heterogeneity and correlation.
The balance of panel data means that each individual has the same number of observations during the observation period. For example, if the observation period is 5 years, then each individual should have 5 observations. The heterogeneity of panel data means that there may be differences between different individuals, such as personal income and enterprise scale. The correlation of panel data refers to the possible correlation between observed values at the same time point, such as the correlation between personal income and education level.
Second, the econometric model of panel data
The econometric model of panel data can be divided into fixed effect model and random effect model. The fixed effect model assumes that the influence of individual characteristics on the dependent variable is fixed and does not change with time. The random effect model assumes that the influence of individual characteristics on dependent variables is random and may change with time. Both models can be estimated and inferred by fixed effect model and random effect model.
Thirdly, the econometric analysis method of panel data.
Econometric analysis methods of panel data include descriptive statistical analysis, regression analysis of panel data and estimation and inference of panel data model. Descriptive statistical analysis can describe the basic characteristics of panel data by calculating the mean, variance and correlation coefficient of panel data. Panel data regression analysis can study the influence of individual characteristics on dependent variables by establishing panel data regression model.
Fourthly, the application case of panel data.
Taking the income data of China residents as an example, this paper introduces the application case of panel data. First, collect the panel data of family income in China, including personal characteristics (such as age, education level, occupation, etc. ), time variables (such as year, city/countryside, etc. ) and dependent variables (such as household income). Then descriptive statistical analysis is carried out to calculate the mean, variance and correlation coefficient of panel data. Next, a panel data regression model is established to study the influence of personal characteristics on family income. Finally, the panel data model is estimated and inferred, and the influence degree and significance of personal characteristics on family income are obtained.