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Topics in Microeconometrics University of Queensland Brisbane, QLD July 7-9, 2010

Descriptive Statistics and Regression. Model Building in Econometrics. Parameterizing the modelNonparametric analysisSemiparametric analysisParametric analysisSharpness of inferences follows from the strength of the assumptions. A Model Relating (Log)Wage to Gender and Experience. Nonparametric RegressionKernel regression of y on x.

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Topics in Microeconometrics University of Queensland Brisbane, QLD July 7-9, 2010

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    1. Topics in Microeconometrics University of Queensland Brisbane, QLD July 7-9, 2010 William Greene Department of Economics Stern School of Business

    2. Descriptive Statistics and Regression

    3. Model Building in Econometrics Parameterizing the model Nonparametric analysis Semiparametric analysis Parametric analysis Sharpness of inferences follows from the strength of the assumptions

    5. Cornwell and Rupert Data

    6. A First Look at the Data Descriptive Statistics Basic Measures of Location and Dispersion Graphical Devices Histogram Kernel Density Estimator

    8. Histogram for LWAGE

    9. Kernel Estimator for LWAGE

    10. The kernel density estimator is a histogram (of sorts).

    11. Objective What is the impact of education on (log) wage? What is the right model to use to analyze this association?

    12. Simple Linear Regression

    13. Multiple Regression

    14. Specification: Quadratic Effect of Experience

    15. Partial Effects Education: .05544 Experience: .04062 – 2*.00068*Exp FEM -.37522

    16. Model Implication: Effect of Experience and Male vs. Female

    17. Hypothesis Test About Coefficients Hypothesis Null: Restriction on ß: Rß – q = 0 Alternative: Not the null Approaches Fitting Criterion: R2 decrease under the null? Wald: Rb – q close to 0 under the alternative?

    18. Hypotheses

    19. Hypothesis Test Statistics

    20. Hypothesis: All Coefficients Equal Zero

    21. Hypothesis: Education Effect = 0

    22. Hypothesis: Experience Effect = 0

    23. A Robust Covariance Matrix What does robustness mean? Robust to: Heteroscedasticty Not robust to: Autocorrelation Individual heterogeneity The wrong model specification

    24. Robust Covariance Matrix

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