截面和面板數(shù)據(jù)分析課件5(復(fù)旦大學(xué)陸銘張晏)-全文預(yù)覽
【正文】 ved attributes of the units.) Pooled Cross Sections ? Pooling cross sections from different years。 ? Effectively analyzing the effects of a new govt. policy。 education。 WLS ? educ? interaction effects (P. , IV) Has the effect of education on fertility rates changed over time? The Chow Test for Structural Change Across Time ? One form of the test obtains the sum of squared residuals from the pooled estimation as the restricted SSR. The unrestricted SSR is the sum of the SSRs for the two separately estimated time periods. ? Another way: interacting each variable with a year dummy for one of the two years and testing for joint significance of the year dummy and all of the interaction terms. ? Usually, after an allowance for intercept difference, certain slope coefficients are tested for constancy by interacting the variable of interest with year dummies. . Changes in the return to education and the gender wage gap ? Econometric Model: ? nominal vs. real value ? Provided the dollar amounts appear in logarithmic form and dummy variables are used for all time periods (except, of course, the base period), the use of aggregate price deflators will only affect the intercepts。 ? age。 type of injury Twoperiod Panel Data Analysis ? Two types of unobserved factors affecting the dependent v. in the panel data： ? keep constant: unobserved effect (fixed effect) ? vary over time: idiosyncratic error (timevarying error) ? Estimation ? pooled cross sections。 if RE is used, then the estimators are generally inconsistent. Hausman Test: Random Effects or Fixed Effects? ?Comparing the FE and RE estimates can be a test for whether there is correlation between the ai and the xitj, assuming that the diosyncratic errors and explanatory variables are uncorrelated across all time periods. ?Hausman Test: Steps for Panel Data Analysis ?Group Effects Test： ?Hausman Test: Example The Return to Education over Time References ?Jeffrey M. Wooldridge, Introductory Econometrics—— A Modern Approach, Chap 13. 。 timevarying error) ? Key Assumption (strict exogeneity): ? That is, the explanatory variables are strictly exogenous after we take out the unobserved effect, ai. ? Cases when strict exogeneity be false: ? If xitj is a lagged dependent variable. ? If we have omitted an important timevarying variable ? Measurement error in one or more explanatory variables Differencing ? Differencing: ? When T is small relative to N, we should include a dummy variable for each time period to account for secular changes that are not being modeled. ? The total number of observations is N(T1) if the data sets are balanced. The differences for t=1 should be missing values for all N crosssectional observations. Serial Correlation in the FirstDifferenced Equation ?Only when uit follows a random walk will uit be serially uncorrelated. ?If we assume the uit are serially uncorrelated with constant variance, then the correlation between uit and ui,t1 can be shown to be . ?If uit follows a stable AR(1) model, then uit will be serially correlated. Test Serial Correlation in the FirstDi
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