Economics 312:
Empirical Economics III

TAs:
Omar Ahmad Al-Ubaydli; omara@uchicago.edu
Ricardo Alvelino; avelino@uchicago.edu
Sebastian Gay; sgay@uchicago.edu
Maria Tripolski; tripolsk@uchicago.edu
Sergio Urzua; surzua@uchicago.edu

TA Sessions: Judd 126 -- M, W 4:00 PM - 5:00 PM & HM 130 -- F 3:30 PM -4:50 PM

go to Econ 312
go to Problem Sets

This is the Website for this course http://lily.src.uchicago.edu/econ312_2003 where lecture notes are posted. There is a final, a midterm and weekly problem sets.

The goal for this course is to acquaint students with the rudiments of modern microeconometrics and methods to build empirical models using microeconomic data.

I. Causal Models and Policy Evaluation Problems: What are the Parameters of Interest?

Haavelmo, T. (1943). "The Statistical Implications of a System of Simultaneous Equations," Econometrica, vol. 11, no. 1, p. 1-12.

Heckman, J. (2001). "Microdata Heterogeneity and The Evaluation of Public Policy," Nobel Lecture, December 2000, Journal of Political Economy , August, 2001. Handouts

Heckman, J. and Vytlacil, E., "Causal Parameters, Structural Equations, Treatment Effects and Randomized Evaluation of Social Programs" , unpublished paper, June, 2001.

II . Identification Problems and Estimation Problems
Alternative Identification Methods

1. Randomized Trials
Heckman, J. (1992). "Randomization and Social Policy Evaluation," paper presented at Institute For Research on Poverty conference at Arlie House in Charles Manski and Irwin Garfinkel, (eds.), Evaluating Welfare and Training Programs , (Harvard University Press, 1992), 201-230.

Heckman, J., N. Hohmann, M. Khoo and J. Smith (2001). "Substitution and Dropout Bias in Social Experiments: A Study of an Influential Social Experiment," Quarterly Journal of Economics , (May), 651-690.

Heckman, J., R. LaLonde and J. Smith (1999). "The Economics and Econometrics of Active Labor Market Programs," in 0. Ashenfelter and D. Cards, eds., Handbook of Labor Economics , North Holland, Chapter 31, 1865-2089. Chapter 1-6 , Sections 7-10 , References , tables & figures

Heckman, J. and J. Smith (1995). “Assessing The Case for Social Experiments,” Journal of Economic Perspectives , 9(2), 85-110.

2. Matching
Heckman, J., Ichimura, H. and Todd, P. (1997). "Matching As An Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," Review of Economic Studies, 64, 605-654.

Heckman, J. and S. Navarro (2003). "Using Matching, Instrumental Variables and Control Functions to Estimate Economic Choice Models," forthcoming, Review of Economics and Statistics .

LaLonde, R. (1986). “Evaluating The Econometric Evaluations of Training Programs with Experimental Data,” American Economic Review, 604-620.

Smith, J. and P. Todd (2001). "Reconciling Conflicting Evidence On the Performance of Propensity Score Matching Methods," American Economic Review , 112-118.

3. Instrumental Variables

Card, D. (2001). "Estimating the Return to Schooling: Progress on Some Persistent Econometric Problems," Econometrica , 69(5), 1127-60.

Carneiro, P., J. Heckman and E. Vytlacil (2001). “Estimating the Rate of Return to Schooling When It Varies Among Individuals”, unpublished manuscript, U of Chicago, April, 2001.

Griliches, Z. (1977). “Estimating The Returns To Schooling: Some Econometric Problems,” Econometrica , 45, 1-22.

Heckman, J. and E. Vytlacil (1998). "Instrumental Variables Methods For the Correlated Random Coefficient Model: Estimating The Average Rate of Return to Schooling When the Return Is Correlated With Schooling," Journal of Human Resources , 33(4), (Fall 1998), 974-1002.

Heckman, J. and E. Vytlacil (2000). “Local Instrumental Variables” in Cheng Hsiao, Kimio Morimune, and James Powell, eds., Nonlinear Statistical Modeling: Proceedings of the Thirteenth International Symposium in Economic Theory and Econometrics, (Cambridge, Cambridge University Press).

Imbens, G. and J. Angrist (1994). “Identification and Estimation of Local Average Treatment Effects,” Econometrica , 62(4), 467-476.

Rosenzweig, M. and K. Wolpin (2000). "Natural "Natural Experiments" in Economics," Journal of Economic Literature , Vol. 308, 827-874.

4. Control Functions: Self Selection Models and Discrete Choice

Blundell, R., R. Reed and T. Stoker and J. Russell (1999). “Interpreting Movements in Aggregate Wages: The Role of Labor Market Participation,” unpublished manuscript, UCL, London.

Chandra, A. (2003). "Is The Convergence In the Racial Wage Gap Illusory?," NBER Working Paper, January.

Domencich, T., and D. McFadden (1975). Urban Travel Demand , (North-Holland), Chapters 4.5.

Heckman, J. (1975). "Shadow Prices, Market Wages and Labor Supply Revisited: Some Corrections and Computational Simplifications," mimeo (June).

Heckman, J. and G. Sedlacek (1985). "Heterogeneity, Aggregation and Market Wage Functions: An Empirical Model of Self-Selection in the Labor Market," Journal of Political Economy, 93(6), 1077-1125.

Heckman, J. (1985). “Selection Bias and Self-selection,” The New Palgrave: A Dictionary of Economics, (MacMillan Press, Stockton, New York), 287-296. (new)

Heckman, J. and B. Honore (1990). “The Empirical Content of the Roy Model,” Econometrica, 58(5), 1121-1149.

Heckman, J. and T. MaCurdy (1981). "New Methods for Estimating Labor Supply Functions: A Survey," Research in Labor Economics , Vol. 4, 65-102.

Heckman, J. and R. Robb (1986). "Alternative Methods For Solving The Problem of Selection Bias in Evaluating The Impact of Treatments on Outcomes," in Howard Wainer, ed Drawing Inference From Self Selected Samples, Springer-Verlag, reprinted 2001,

Todd, P. (1999). “Notes on Kernel Regression and Local Linear Regression,” University of Pennsylvania.

Willis, R. and S. Rosen (1979). "Education and Self Selection," Journal of Political Economy, 87, S7-S36.

5. Comparisons Across People: Twin Models, Ability Bias and Measurement Error

Ashenfelter, O., and A. Krueger (1994). “Estimates of the Economic Returns to Schooling From A New Sample of Twins,” American Economic Review , 84(5), 1157-1173.

Bound, J., and G. Solon (1999). “Double Trouble,” Economics of Education Review , Fall Issue.

6. Panel Data Methods

Arellano, M. and B. Honore (2000). “Panel Data” in J. Heckman and E. Leamer, Handbook of Econometrics , Vol. V, (North Holland).

Blundell, R., A. Duncan and C. Meghir (1998). “Estimating Labor Supply Responses Using Tax Policy Reforms,” Econometrica , 66, 827-801.

Chamberlain, G. (1985). "Panel Data," Chapter 22, Handbook of Econometrics , Vol. II.

Griliches, Z. and J. Mairesse, (1998). “Production Functions: The Search For Identification,” in S. Strom, ed., Econometrics and Economic Theory in the 20th Century , (Cambridge, Cambridge University Press), pp. 169-203.

Heckman, J. and R. Robb (1985). “Using Longitudinal Data to Estimate Age, Period and Cohort Effects in Earnings Equations,” in William M. Mason and Stephen E. Feinberg, eds., Cohort Analysis in Social Research Beyond the Identification Problem, Springer-Verlag, New York.

Heckman, J. and J. Smith (1999). "The Pre-Programme Earnings Dip and the Determinants of Participation in A Social Programme Implications For Simple Programme: Evaluation Strategies," Economic Journal , 109(457): 1-37.

Hsiao, C. (1986) . Panel Data , Chapters 1, 2, 3, (Cambridge: Cambridge University Press).

MaCurdy, T. (2001). "A Practitioner's Approach for Modeling Wage Dynamics: Micro-Longitudinal and Pooled Cross-Section/Time Series Analyses," forthcoming in J. Heckman and E. Leamer, Handbook of Econometrics , Vol. 6, Amsterdam: North Holland.

III. Dynamic Models: Heterogeneity vs. State Dependence

Cameron, S. and J. Heckman (1998). “Life Cycle Schooling and Dynamic Selection Bias: Models and Evidence for Five Cohorts of American Males,” Journal of Political Economy , 106(2), 262-311.

Cameron, S. and J. Heckman (2001). “The Dynamic Models of Schooling Attainment for Blacks, Whites and Hispanics,” 1992, revised 1998, Journal of Political Economy , June, 2001.

Eckstein, Z. and K. Wolpin (1989). “The Specification and Estimation of Dynamic Discrete Choice Models,” Journal of Human Resources , 24, 562-598.

Flinn C. and J. Heckman (1982). “Models For the Analysis of Labor Force Dynamics,” in R. Bassman and G. Rhodes, Advances in Econometrics , Vol. 1, (JAI Press) 65-69.

Flinn C. and J. Heckman (1982). “New Methods For Analyzing Structural Models of Labor Force Dynamics,” Journal of Econometrics, 18, 115-168.

Flinn C. and J. Heckman (1983). “The Likelihood Function,” in Advances in Econometrics , 2, 225-231, ed. By R. Bassman and G. Rhodes, (JAI Press).

Heckman, J. (1981). “Statistical Models for Discrete Panel Data,” in C. Manski and D. McFadden (eds), Structural Analysis of Discrete Data With Econometric Applications, (M.I.T. Press), Chapter 3 and 4.

Heckman, J. (1982). "Heterogeneity and State Dependence," in S. Rosen (ed.), Studies in Labor Markets , (University of Chicago Press, 1981), 91-139.

Lancaster, T. (1991). The Econometric Analysis of Transition Data , Chapters 1-4, Cambridge: Cambridge University Press.

IV. Testing, Pretesting and Model Selection

Leamer, E. (1978). Specification Searches , Wiley, New York, pessim.

V. Calibration vs. Estimation

Browning, M., L. Hansen and J. Heckman (1999). "Micro Data and General Equilibrium Models," in J. Taylor and M. Woodford (eds), Handbook of Macroeconomics , (Amsterdam: Elsevier), Chapter 8, 543-633.

Hansen, L. and Heckman, J. (1996). "The Empirical Foundations of Calibration," The Journal of Economic Perspectives , 10(1), Winter, 87-104 .

Judge, G. W. Griffiths, R. Hill and T. Lee (1980). The Theory and Practice of Econometrics , (Wiley), Chapter 13, “Unobserved Variables”.

Kydland, F. and Prescott , E. (1996). "The Computational Experiment: An Econometric Tool", (in Symposia: Computational Experiments in Macroeconomics) The Journal of Economic Perspectives , Vol. 10, No. 1. (Winter, 1996), pp. 69-85.

Sims, C. (1996). "Macroeconomics and Methodology," (in Symposia: Computational Experiments in Macroeconomics), The Journal of Economic Perspectives, Vol. 10, No. 1. Winter, pp. 105-120.