F检验判断是混合（mixed effect）效应还是固定效应（即是否存

The F test determines whether it is a mixed effect or a fixed effect (that is, whether there is βi0). The original hypothesis is that there is no fixed effect. <br>13 Random effect regression. <br>Each individual has a different random error term, which is not related to t, and is related to the individual. (Although it is related to the individual, but it is random, and the intercept term of the fixed effect is also related to the individual, but it is fixed) <br>Solution: the least squares estimate of the average, that is, to average different individuals, then the average random error term is different There is no difference in time, so it is equivalent to a constant. <br>Hausman test for fixed effects or random effects <br>14 Heterogeneity (heteroskedastic): individual heterogeneity: cross-sectional data have different characteristics. <br>Should fixed effects be tested for heteroscedasticity? why? What if I do?

The F-test determines whether the mixed effect or fixed effect (i.e. the presence of beta i0) and the assumption is that there is no fixed effect<br>13 Random Effect Regression<br>Each individual has a different random error term, not related to t, not related to the individual (although related to the individual, but random, and the fixed effect of the intercept term is also related to the individual, but fixed)<br>Solution: Average least-by-second estimate, which averages different individuals, then the average random error term is no different to the different points in time, so it is equivalent to a constant.<br>Haussmann tests whether fixed or random effects<br>14 Heteroskedastic: Individual Heterogenectivity: Cross-sectional data have different characteristics<br>Does the fixed effect have to be tested for heterovariance? Why? What if i do it?

F test is used to determine whether the mixed effect or the fixed effect exists (i.e. whether there is β I0). The original hypothesis is that there is no fixed effect<br>13 random effect regression<br>Each individual has a different random error term, which is independent of t but related to the individual (although it is related to the individual, it is random, and the intercept term of fixed effect is also related to the individual, but fixed)<br>Solution: mean least square estimation, that is to say, different individuals are averaged, so the average random error terms are not different for different time points, so they are equivalent to constants.<br>Hausmann test fixed effect or random effect<br>Heterogeneity: individual heterogeneity: cross sectional data have different characteristics<br>Is heteroscedasticity test necessary for fixed effects? Why? What if you do?<br>