After completing cross-sectional re

After completing cross-sectional regression for each phase, the regression statistics of each phase will be obtained, and the time series of intercept terms and coefficients will also be obtained.4.2.1.2 Calculate the mean value of time series of cross-section regression resultsThe second step of Fama-Macbeth regression is to calculate the time series averages of the cross-sectional regression coefficients and other regression statistics for each period. The standard error of the coefficient estimate and the corresponding T-statistic are then calculated to test whether the mean value of the regression coefficient is significantly non-zero.4.2.2 Interpretation of regression resultsFama-macbeth's interpretation of regression results is intuitive. If there is a significant cross-sectional relationship between the explanatory variable X and the explained variable Y, the mean value of the regression coefficient is significant. If the regression equation contains multiple explanatory variables and the mean value of the regression coefficient of X is significant, it indicates that after controlling the other explanatory variables in the equation, there is a significant cross-sectional relationship between X and Y. However, if the regression coefficient of X is significant and not significant after the addition of other control variables, it means that the cross-sectional relationship between X and Y has been explained by the new linear combination of control variables. Similarly, if the regression coefficient of X is not significant, but becomes significant when other control variables are added, it means that the influence of newly added control variables needs to be controlled when studying the cross-sectional relationship between X and Y.How much of the total change in the variable is explained by the explanatory variables in the model can be represented by the mean of the time series of R^2 and the adjusted R^2.Fama-macbeth regression is applicable to study the cross-sectional relationship between one or more explanatory variables and explained variables. Its advantage is that it can control several control variables with potential influence on explanatory variables at the same time, while its main disadvantage is that it needs to assume a linear relationship between explanatory variables and explained variables.