截面和面板數(shù)據(jù)分析課件3(復(fù)旦大學(xué)陸銘張晏)(文件)
【正文】 ng whether the estimated difference between single and married women is statistically significant. to choose one of these groups to be the base group and to reestimate the equation. ? Interpretation: Example (,) The Determination of log Hourly Wage: Interaction Effects Involving with Dummy Variables ? Adding Interaction Term ? The marriage premium depends on gender ? the rest of the regression is necessarily identical to (). ? Equation () is just a different way of finding wage differentials across all gendermarital status binations. It has no real advantages over ()。 experimental (treatment) group ? be careful to include factors that might be systematically related to the binary independent variable of interest. ? SelfSelection Problems: ? The term is used generally when a binary indicator of participation might be systematically related to unobserved factors. ? another way that an explanatory variable can be endogenous. ? Solutions: ? Data ? more advanced methods Example: the effect of the job training grants on worker productivity ?Consider again the Holzer et al. (1993) study, where we are now interested in the effect of the job training grants on worker productivity (as opposed to amount of job training, example ). References ?Jeffrey M. Wooldridge, Introductory Econometrics——A Modern Approach, Chap 4－ 7. 。 the R2 form of the test can be used only if interactions have been included to create the unrestricted model. ? One important limitation of the Chow test: regardless of the method used to implement it, is that the null hypothesis allows for no differences at all between the groups. Policy Analysis and Program Evaluation with Dummy Variables ? Policy analysis。 (0,1,0)。 (1,1)。 (0,1,0)。 (0,1)。 ? population, total number of employees, and school enrollment ? Variables that are measured in years—such as education, experience, tenure, age, and so on—usually appear in their original form. ? A variable that is a proportion or a percent—such as the unemployment rate, the participation rate in a pension plan, the percentage of students passing a standardized exam, the arrest rate on reported crimes—can appear in either original or logarithmic form, although there is a tendency to use them in level forms. ? a percentage change and a percentage point change More on Taking Logs ? If a variable takes on zero or negative values: ? The percentage change interpretations are often closely preserved, except for changes beginning at y=0 (where the percentage change is not even defined) ? One drawback to using a dependent v. in logarithmic form is that it is more difficult to predict the original v. ? it is not legitimate to pare R2 from models where y is the dependent v. in one case and log(y) is the dependent v. in the other. ? (see Section ) Models with Quadratics ? Decreasing or Increasing Marginal Effects: Quadratic Functions ? parabolic shape and Ushape ? Turning point ? If this turning point is beyond all but a small percentage of the v. in the sample, then this is not of much concern. ? Other forms: ? using quadratics along with logarithms. ? some care is needed in making a useful interpretation with a dependent v. in log form and an explanatory v. entering as a quadratic ? a nonconstant elasticity: doublelog and quadratics ? other polynomial terms: a cubic and even a quartic term Models with Interaction Terms ? Interaction Effects: ? Sometimes it is natural for the partial effect, elasticity, or semielasticity of the dependent v. with respect to an explanatory v. to depend on the magnitude of yet another explanatory v. Example : Effects of Attendence on Final Exam Performance ? Dependent v.: Final Exam Performance Standardized oute on a final exam (stndfnl) ? Be easier to interpret a student’s performance relative to the rest of the class ? Explanatory : ? Percentage of classes attended (atdrte) ? prior college grade point average (priGPA), and ACT score ? Functional Form: ? Quadratics in priGPA and ACT ? class attendance might have a different effect for students
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