Lecture 15 - PAM 3300: Midterm Review Where weve been...

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Unformatted text preview: PAM 3300: Midterm Review Where weve been Overview of Program Evaluation and Program Theory (ln2) Statistics Review (ln3) Causality - FPCI, counterfactual reasoning, statistical approach to solving FPCI, drawbacks of PF causal effect estimator, attributes as causes, SUTVA (ln4) Regression Review, description, uses of R 2 , causal interpretations of coefficients, omitted variable bias, multiple regression (ln5-7) Research designs and validity, randomized controlled trials (ln8) Rand Health Insurance Experiment (ln8-9) NYC Voucher Experiment (ln9-10) Non-experimental designs (ln10-11) Munnel et al. Study of Mortgage Lending in Boston (ln 11) Krueger vs. DiNardo & Pischke Studies of Returns to computer use (ln12) Assessment of non-experimental designs by LaLonde (ln13) Regression Regression as a linear approximation to graph of averages Summarizing the relationship: Regression of Earnings on Years of Education E[ Y i | X i ]=-19,280+4,157 X i Regression line: linear approximation to graph of averages Regression Language - Interpreting coefficients (1) Y and X are continuous (regress wage on years education) Interpretation of constant: The average hourly wage of those with 0 years of school is -15.25. Interpretation of coefficient on school: Each additional year of education is associated with an extra $2.67 in hourly wages on average. Avoid words like each additional year of school causes/increases/leads to unless you believe the relationship is causal. . reg hrwagely school Source | SS df MS Number of obs = 1000-------------+------------------------------ F( 1, 998) = 127.30 Model | 53501.4245 1 53501.4245 Prob > F = 0.0000 Residual | 419437.138 998 420.277693 R-squared = 0.1131-------------+------------------------------ Adj R-squared = 0.1122 Total | 472938.562 999 473.411974 Root MSE = 20.501------------------------------------------------------------------------------ hrwagely | Coef. Std. Err. t P>|t| [95% Conf. Interval]-------------+---------------------------------------------------------------- school | 2.670268 .2366684 11.28 0.000 2.205843 3.134693 _cons | -15.25841 3.230216 -4.72 0.000 -21.5972 -8.919613------------------------------------------------------------------------------ Regression Language - Interpreting coefficients (2) Y and X are continuous, Y in logs (regress log wages on years education) Interpretation of constant: The average log hourly wage of those with 0 years of school is 1.31989. Interpretation of coefficient on school: Each additional year of education is associated with about 10.61% higher hourly wages on average. . reg lnw school Source | SS df MS Number of obs = 1000-------------+------------------------------ F( 1, 998) = 210.91 Model | 84.5172482 1 84.5172482 Prob > F = 0.0000 Residual | 399.919216 998 .400720658 R-squared = 0.1745-------------+------------------------------ Adj R-squared = 0.1736 Total | 484.436465 484....
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Lecture 15 - PAM 3300: Midterm Review Where weve been...

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