Applied Microeconometrics.

Professor: Ruben Enikolopov

Teaching Assistant:

The goal of the courseis to familiarize you with a range of techniques used in applied microeconometrics. The emphasis in the course will be on issues that arise in working with data and practical considerations in using various econometric techniques rather than their theoretical underpinning. It is assumed that you already have a sufficient knowledge of the basic econometric theory.

Grading

The grade for the course will be based on three computer based problem sets (60%) and final exam (40%). The problem sets will involve analysis of the data using the methods discussed in class. The examwillcontain questions on a published paper in applied microeconometrics handed out in advance.

Books

Wooldridge, J. “Econometric Analysis of Cross Section and Panel Data” (JW)

Cameron, C. and Trivedi, P. “Microeconometrics: Methods andApplications” (CT)

Syllabus

The reading list includes papers that will be discussed in class or provide good discussion of the relevant topics. Reading the papers is not required, but is highly recommended.

  1. Research design. Causality. Potential outcomes approach. CT Ch 2, 25; JW Ch 18.
  2. Angrist and Krueger (1999) “Empirical Strategies in Labor Economics” in Handbook of Labor Economics, vol. 3A.
  3. Krueger, A. (1993) “How Computers Have Changed the Wage Structure: Evidence from Micro Data,” Quarterly Journal of Economics, 108 (1), 33-60.
  4. DiNardo, J., and Pischke, J.-S. (1997) “The Returns to Computer Use Revisited: Have Pencils Changed the Wage Structure Too?” Quarterly Journal of Economics 112(1), 291-303.
  5. Measurement error problems. CT Ch 16.
  6. Griliches, Zvi. (1986) "Economic Data Issues," in Handbook of Econometrics, Volume III.
  7. Hausman, J. (2001) “Mismeasurement Variables in Econometric Analysis: Problems from the Right and Problems from the Left,” Journal of Economic Perspectives, 15(4), 57-67.
  8. Panel data. Fixed effects vs. random effects vs. clustering. Difference-in-Difference estimators. CT Ch 21, 22.
  9. Chamberlain G. ( 1987) “Panel data” in Handbook of Econometrics.
  10. Aschenfelter, O. and Krueger,A. (1994) “Estimates of economic return to schooling from a new sample of twins,” American Economic Review, 84(5), 1157-1173.
  11. Donohue, J., and S. Levitt (2001) ‘‘The Impact of LegalizedAbortion on Crime,’’ Quarterly Journal of Economics, 116(2), 379–420.
  12. Joyce, T. (2004) ‘‘Did Legalized Abortion Lower Crime,’’ 39(1) Journal of Human Resources 1–28.
  13. Donohue, J., and S. Levitt (2004) ‘‘Further Evidence that Legalized Abortion Lowered Crime: A Replyto Joyce,’’ 39 Journal of Human Resources 29–49.
  14. Foote, Christopher L., and Christopher F. Goetz. (2005) ‘‘Testing EconomicHypotheses with State-Level Data: A Comment on Donohue and Levitt.’’
  15. Donohue, John J., III and Steven D. Levitt (2006), “Measurement Error, Legalized Abortion, and theDecline in Crime: A Response to Foote and Goetz (2005),” NBER WP #11987.
  16. Dills, A., and Miron, J. (2006) “A Comment on Donohue and Levitt’s (2006) Reply to Foote and Goetz (2005)”
  17. Bertrand, M., Duflo, E. and Mullainathan, S. (2004) “How Much Should We Trust Difference in Differences Estimates?” Quarterly Journal of Economics 119, 249-275.
  18. Instrumental Variables. Two main assumptions. Weak Instruments problem. CT Ch 4, JW Ch 5.
  19. Angrist, J. and Krueger, A. (1991) “Does compulsory schooling attendance affect schooling and earnings?” Quarterly Journal of Economics 106, 979-1014.
  20. Bound, J., Jaeger, D., and Baker, R. (1995) “Problems with instrumental variable estimation when the correlation between the instruments and the endogenous explanatory variable is weak,” Journal of the American Statistical Association 90 (430), 443-450.
  21. Staiger, D., and J. Stock (1997): “Instrumental variables regression with weakinstruments,” Econometrica, 65, 557-86.
  22. Moreira, M. and L. Cruz (2005) “On the Validity of Econometric Techniques With Weak Instruments: Inference on Returns to Education Using CompulsorySchool Attendance Laws,” Journal of Human Resources, 40(2), 393-410.
  23. Random coefficients. Implications for IV estimation and matching vs. regression. CT Ch 25, JW Ch 18.
  24. Angrist, J. (1998) “Estimating the labor market impact of voluntary military service using social security data on military applicants,” Econometrica, 66(2), 249-288.
  25. Imbens, G. and J. Angrist (1994) “Identification and Estimation of Local Average Treatment Effects," Econometrica, 62, pp. 467-75.
  26. Angrist, J.D., G.W. Imbens and D.B. Rubin (1996), “Identification of CausalEffects Using Instrumental Variables,” (with discussion) Journal of the American StatisticalAssociation, 91, 444-472.
  27. Refutability. Placebo regressions.
  28. Card, D. (1990) “The impact of the Mariel boatlift on the Miami labor market,” Industrial and Labor Relations Review, 43, 245-257.
  29. Enikolopov, R., Petrova, M. and Zhuravskaya, E. (2008) “Media and Political Persuasion in Young Democracies: Evidence from Russia”, working paper.
  30. Discontinuity analysis. CT Ch 25.
  31. DiNardo, J., and D.S. Lee, (2004) “Economic Impacts of New Unionization on Private Sector Employers: 1984-2001,”Quarterly Journal of Economics 119, 1383-1441.
  32. Lee. D, Moretti, E., and Butler M. (2004) “Do Voters Affect or Elect Policies? Evidence from the U.S. House,” Quarterly Journal of Economics, 119(3), 807-859.
  33. Limited depended variables. CT Ch 14-16, JW Ch 15, 16.
  34. Chay, K. and J. Powell (2001) “Semiparametric Censored Regression Models,"Journal of Economic Perspectives, 15, 29-42.
  35. Powell, J. (1984) “Least Absolute Deviations Estimation for the Censored Regression Model," Journal of Econometrics, 25, 303-25.
  36. Powell, J. (1986b) ‘Symmetrically Trimmed Least Squares Estimation for Tobit
  37. Models," Econometrica,” 54, 1435-60.
  38. Billett, Matthew T., and Hui Xue, 2006, The Takeover Deterrent Effect of Open Market Share Repurchases, Journal of Finance, forthcoming.
  39. Incidental parameter problem. CT Ch 23.2; JW Ch 15.8
  40. Lancaster, T. (2000) “The Incidental Parameter Problem Since 1948," Journal of Econometrics, 95, 391-413.
  41. Sample selection. JW Ch 17.
  42. Heckman, J. (1990) “Varieties of Selection Bias”, American Economic Review, 80, 313-318.
  43. Heckman, J. (1979) “Sample Selection Bias as a Specification Error”, Econometrica, 47, 153-161.
  44. Quantile regressions.
  45. Koenker, R., and K. Hallock (2001) “Quantile Regression,” Journal of Economic Perspectives, 15, 143-156.
  46. Angrist, J., Bettinger, E., and M. Kremer (2005) "Long-Term Educational Consequences of Secondary School Vouchers: Evidence from Administrative Records in Colombia," forthcoming in American Economic Review.