Economics 312, Spring 2023, Part A: COMBINED PRIVATE KEY

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Instructor: James J. Heckman

• Lecture times: Tuesdays and Thursdays, 5:00pm – 6:20pm
• Lecture classroom: Saieh Hall, Room 146
• Teaching Assistants:
  • Deniz Dutz (ddutz@uchicago.edu)
  • Sofia Shchukina (sofiashchukina@uchicago.edu)
• TA Sessions:
  • Friday, 3:30 – 4:20, Room Saieh 146
  • Office hours by appointment
  • NOTE: Please email the TA if you plan to attend their Office Hours so that they do not wait unnecessarily. Please email them before office hours with any specific questions so that they can prepare.

If you experience problems with this website, please contact Jennifer Pachon.

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Course Description

This course examines how economists learn from data. We focus on the following topics:

1. Counterfactuals and economic policy evaluation in economics and statistics
2. Alternative modes of inference, understanding how economists approach causal questions (abduction, exploration and discovery)
3. Using economics to synthesize and interpret data

Goals of the Course

1. Understand that econometrics is a field rooted in economics. Econometrics is much more than statistics and much more than just hypothesis testing:
   1. Using economics to organize and interpret data and to motivate choices of estimators and testing procedures
   2. Using economics and data to address policy problems
      1. Different classes of policy problems pose different challenges
      2. “Causal parameters” vs. “structural parameters”: is there any useful distinction?
   3. Understand the consequences of how data are generated (sampling plans) and how to account for them
   4. Replicability and consilience are essential activities of economic science
   5. Alternative modes of inference: classical statistics and its limitations; Bayesian and likelihood alternatives; abduction.
2. Tools
   1. Economic models to organize and interpret evidence
   2.  Comparison of estimation methods in the context of Generalized Roy Models and extensions:
      1. Structural methods
      2. IV
      3. Matching
      4. Control functions
      5. Longitudinal data and difference-in-differences
      6. Duration models

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Class Requirements

There will be a written exam assigned on April 17 and due April 24. There will also be 5 problem sets that are due each week. They will be graded and count toward the final grade. The assignments will include analytical, free-response, and empirical questions. These questions will require the use of programming languages like Python, R, or MATLAB. Any programming language is accepted for the simulation exercises. If students have any questions on Problem Sets they should first ask the TA and only ask the professor if the TA is unable to help.

For the problem sets in Part A of the course, you may form groups of up to 3 people, maximum, with no exceptions. These study groups will be permanent for the rest of the course.

Please enter your group into this google sheet by Friday, March 24: 

link to form groups

Each week, please have (only) one of your group members upload their problem set to Canvas (no late submissions are accepted) before the deadline. Note that groups consisting of more than 3 members earn a mark of 0.

Rules for submission:

• Include everyone’s names in the submitted document
• Unless specified, the deadline for each assignment is by Monday at 11:59pm.
• The documents containing the write-up (including but not limited to paragraph answers, equations, graphs, plots, diagrams, tables) must be in PDF format and you are strongly encouraged to use LaTeX to typeset your solutions. A collaborative platform like Overleaf would be useful
• Please submit your code along with the write-up: both the source file(s) and the PDF version of the code if possible. Platforms like RMarkdown (for R), Jupyter (for Python and R) and MATLAB live scripts can be especially useful to include equations and text in Markdown cells alongside code blocks. The code should be well-formatted, with comments and well-labeled variable names as appropriate

Your grade for part A of the course will depend on the problem sets (50%) and the take home exam (50%). Bonus points will be awarded for participation during class.

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Lecture Notes

Lecture notes for each week will be posted on the Canvas site in advance of each lecture on the website. The handouts distill and complement the readings.

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2023 Syllabus

Recommended readings are indicated by (*). All other readings on this list and the supplement are background.

Topic 1. Learning from Data

1. Overview and Plan of the Course
2. Learning from Data
3. Abducting Economics: How to Learn from Surprises, Heckman and Singer (2016)
4. Friedman’s Approach to Empirical Economics
5. (*)  Hypothesis Testing: Part I
6. (*)  Econometric Approach to Causality, Heckman and Pinto, 2022
7. Econometric Policy Analysis

Topic 2. Causality

• (*)  Econometric Approach to Causality, Heckman and Pinto, 2022
• Heckman, James J., and Rodrigo Pinto. (2022). “The Econometric Model for Causal Policy Analysis.” Annual Review of Economics, 14(1):893-923.
  • What is a causal effect? How to express it? And why it matters. [needs retitle if used]
• (*)Heckman, James J. (2008). “Econometric Causality,” International Statistical Review, 76(1): 1-27.
  • Econometric Causality: Part I on Causality
  • Full version: Econometric Causality [needs retitle if used]
• Heckman, James J., and Rodrigo Pinto. (2022). “Causality and Econometrics.” NBER Working Paper No. 29787
  • Causality and Econometrics: Part I by Heckman and Pinto (2022) [needs retitle if used]
  • Causality and Econometrics: Part II by Heckman and Pinto (2022) [needs retitle if used]
  • Full version: Causality and Econometrics by Heckman and Pinto (2022) [needs retitle if used]
• Causality in Econometrics and Statistics: Structural Models are Causal Models Some Formal Statements Part III on Causality [needs retitle if used]
• Causality in Econometrics and Statistics: Structural Models are Causal Models (Do-Calculus Extract) [needs retitle if used]
• (*)Heckman, James J., and Richard Robb. (1985). “Alternative Methods for Evaluating the Impact of Interventions,” In Longitudinal Analysis of Labor Market Data, James J. Heckman and Burton Singer, eds. Cambridge University Press: New York, NY.
  • A Running Example and Extensions Based on “Alternative Methods for Evaluating the Impact of Interventions: An Overview,” Heckman and Robb (1985)

A. Abduction

• (*)Heckman, James J. and Burton Singer. (2017). “Abducting Economics,” American Economic Review: Papers and Proceedings,107(5):298-302.
  • Abducting Economics: How to Learn from Surprises,  Heckman and Singer (2016) [needs retitle if used]
• Singer, Burton. (2008). “Comment: Implication Analysis as Abductive Inference.” Sociological Methodology, 38:75-83.
  • Abduction, Singer (2008) [needs retitle if used]
• Donohue, John J., and James J. Heckman. (1991). “Continuous Versus Episodic Change: The Impact of Civil Rights Policy on the Economic Status of Blacks.” Journal of Economic Literature, 29(4):1603-1643.
  • Continuous Versus Episodic Change…, Donohue and Heckman (1991) [needs retitle if used]
• (*)Humphries, John Eric. (2019). “The Causes and Consequences of Self-Employment over the Life Cycle,” Unpublished manuscript, Yale University, Department of Economics.
  • The Causes and Consequences of Self-Employment over the Life Cycle,  Humphries (2019). [needs retitle if used]
• Friedman, Milton. (1953). “The Methodology of Positive Economics,” in M. Friedman, Essays in Positive Economics. Chicago: University of Chicago Press. See Footnote 11 and surrounding text.
• NOVA. Mystery of a Masterpiece.
• Gilboa, Itzhak, Larry Samuelson, and David Schmeidler. (2015). Analogies and Theories: Formal Models of Reasoning, Lipsey Lectures. Cambridge University Press: New York, NY.

B. Replicability in Economics

• (*)Christensen, Garret, and Edward Miguel. (2018). “Transparency, Reproducibility, and the Credibility of Economics Research.” Journal of Economic Literature, 56(3):920-80.
  • Transparency, Reproducibility, and the Credibility of Economics Research,  Christensen and Miguel (2018)
• Heckman, James J., and Brook S. Payner. 1989. “Determining the Impact of Federal Antidiscrimination Policy on the Economic Status of Blacks: A Study of South Carolina.” The American Economic Review 79 (1): 138–77.
  • Determining the Impact of Federal Antidiscrimination Policy on the Economic Status of Blacks: A Study of South Carolina, Heckman and Payner (1989). [needs update]
• Katz, Rebecca, and Burton Singer. 2007. “Can an Attribution Assessment Be Made for Yellow Rain? Systematic Reanalysis in a Chemical-and-Biological-Weapons Use Investigation.” Politics and the Life Sciences 26 (1): 24–42.

Topic 3: Discrete Choice, Self-Selection and the Generalized Roy Model

• Discrete Dependent Variable Models [needs update]
• Styles of Empirical Research
• Probabilistic Choice Models [needs update]
• Conditional Logit Models [needs update]
• Classical Discrete Choice Theory [needs retitle if used]
• Definition of Samples
• (*)Heckman, James J. (2018). “Selection Bias and Self-Selection.” In The New Palgrave Dictionary of Economics, 12130-12147. London: Palgrave Macmillan UK.
• Roy Models of Policy Evaluation [needs retitle if used]
• The Normal Generalized Roy Model [needs retitle if used]
• Extract: Notes on Roy Models and Generalized Roy [needs retitle if used]
• Notes on Identification of the Roy Model and the Generalized Roy Model[needs retitle if used]
• The Pre-Test Estimator by Heckman and Pinto [needs retitle if used]
• Heckman, James J., and Guilherme 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.
  • Skills and Tasks in the Labor Market, Heckman and Sedlacek (1985) [needs retitle if used]
• Heckman, James. (1974). “Shadow Prices, Market Wages, and Labor Supply.” Econometrica, 42(4):679-694.
  • Shadow Prices, Market Wages and Labor Supply by Heckman (1974) [needs retitle if used]
• Lewbel, Arthur. (2019). “The Identification Zoo: Meanings of Identification in Econometrics.” Journal of Economic Literature, 57(4):835-903.
  • The Identification Zoo: Meanings of Identification in Econometrics [needs retitle if used]
• Eisenhauer, Philipp, James J. Heckman, and Stefano Mosso. (2015). “Estimation of Dynamic Discrete Choice Models by Maximum Likelihood and the Simulated Method of Moments,” International Economic Review, 56(2): 331-357.
  • Rate of Return Continuation Values and Option Values in a Simple Dynamic Model , Eisenhauer, Heckman and Mosso (2015). [needs retitle if used]
• Cunha, Flávio and James J. Heckman. (2016). “Decomposing Trends in Inequality in Earnings into Forecastable and Uncertain Components,” Journal of Labor Economics, 34(S2): S31-S65.
  • Separating Heterogeneity from Uncertainty Decomposing Trends in Inequality in Earnings into Forecastable and Uncertain Components Extract [needs retitle if used]
• How To Correct for Sampling Biases

Topic 4. Instrumental Variables

• (*)Heckman, James J. 2010. “Building Bridges between Structural and Program Evaluation Approaches to Evaluating Policy.” Journal of Economic Literature, 48(2):356-98.
• Heckman, James J., Sergio Urzua, and Edward Vytlacil. (2006). “Understanding Instrumental Variables in Models with Essential Heterogeneity.” The Review of Economics and Statistics, 88(3):389-432.
• Heckman, James J. and Vytlacil, Edward J. (2007). “Econometric Evaluation of Social Programs, Part II: Using the Marginal Treatment Effect to Organize Alternative Economic Estimators to Evaluate Social Programs and to Forecast Their Effects in New Environments,” in Handbook of Econometrics, Vol. 6B, J. Heckman and E. Leamer, eds. Amsterdam: Elsevier. pp. 4875-5144.
  •   Econometric Evaluation of Social Programs Part II, Heckman and Vytlacil (2007)

1. Interpreting IV, Part 1
2. Interpreting IV, More on Roy Model
3. Interpreting IV, LATE
4. RDD
5. Interpreting IV: What Economic Questions Can LATE Answer?
6. MTE as Generator of All Treatment Effects: IV and Policy Weights
7. IV Weights and Yitzhaki’s Theorem
8. Yitzhaki Derived the Weights Used by the Proponents of LATE but Without Citation: The Weights Have a Lot of Intuition
9. Derivation of Other Weights
10. Carneiro, Pedro, James J. Heckman, and Edward J. Vytlacil. (2011). “Estimating Marginal Returns to Education.” American Economic Review, 101(6):2754-81.
    • Some Evidence on the Returns to Schooling, Carneiro, Heckman and Vytlacil (2011)
    • What Does IV Estimate? “Estimating Marginal Returns to Education”, Carneiro, Heckman and Vytlacil (2011)
11. Comparing IV with Structural Models: What Simple IV Can and Cannot Identify

Topic 5. General Principles Underlying All Econometric Estimators

• Heckman, James J. (2008). “The Principles Underlying Evaluation Estimators with an Application to Matching.” Les Annales d’Economie et de Statistique, 91-92, pp. 9-74.
  • The Principles Underlying Evaluation Estimators,  excerpted from Heckman, James J. (2008). “The Principles Underlying Evaluation Estimators with an Application to Matching.” Les Annales d’Economie et de Statistique, 91-92, pp. 9-74.

Topic 6. Matching

• Heckman, James J., Robert J. Lalonde, and Jeffrey A. Smith. (1999). “The Economics and Econometrics of Active Labor Market Programs.” In Handbook of Labor Economics, edited by Orley C. Ashenfelter and David Card, Chapter 31, pp. 1865-2097. Amsterdam: Elsevier.
  • Some Mechanics on the Method of Matching,  Heckman, LaLonde, and Smith (1999)
• Heckman, James J., Hidehiko Ichimura, and Petra Todd. (1998). “Matching As An Econometric Evaluation Estimator.” The Review of Economic Studies, 65(2):261-294.
  • Matching As An Econometric Evaluation Estimator,, Heckman, Ichimura, and Todd (1997)

Topic 7: Simultaneous Equations and Social Interactions

• Simultaneous Causality: Part IV on Causality
• (*)Johnston, J. (1963). “Simultaneous Equation Systems,” in Econometric Methods, 3rd Edition. St. Louis, MO: McGraw-Hill Book Company. Chapter 11, pp. 439-497.
• (*)Moffitt, Robert. (2001). “Policy Interventions, Low-Level Equilibria, and Social Interactions.” In Social Dynamics, edited by Steven N. Durlauf and Peyton Young. Cambridge, MA: The MIT Press. Chapter 3.
  • Policy Interventions, Low level Equilibria and Social Interactions, Moffitt (2001)
• Pierse, Richard G. (2020). “Econometrics Lecture 7: Simultaneous Equations Models: Identification, Estimation and Testing” Unpublished manuscript, National Institute of Economic and Social Research (NIESR).
  • Econometrics Lecture 7: Simultaneous Equations Models: Identification, Estimation and Testing

Topic 8: Longitudinal and Panel Data

• (*)Heckman, James J., and Richard Robb. (1985). “Alternative Methods for Evaluating the Impact of Interventions: An Overview.” Journal of Econometrics, 30(1–2):239-267.
  • Alternative Methods for Evaluating the Impact of Interventions: An Overview, Heckman and Robb (1985)
• Hsiao, Cheng. (2003). Analysis of Panel Data. 2nd ed. Econometric Society Monographs. Cambridge: Cambridge University Press.
• Arellano, Manuel, and Bo Honoré. (2001). “Panel Data Models: Some Recent Developments.” In Handbook of Econometrics, Volume 5. James J. Heckman and Edward Leamer (eds.). Chapter 53, 3229-3296. Amsterdam: Elsevier.
  • Panel Data Models: Some Recent Developments
• Factor Models: A Review
• Factor Models
• Notes on Factor Models and the Hicks Lecture Model with Normal Random Variables
• Meghir, Costas and Luigi Pistaferri. (2011). “Earnings, Consumption and Life Cycle Choices,” In: Orley Ashenfelter and David Card, eds., Handbook of Labor Economics, Vol. 4, Part B. Amsterdam: Elsevier. pp. 773-854.
  • Modeling the Income Process

Slides:

• Discrete Time Panel Data Methods
  1. Ability Bias, Errors in Variables and Sibling Methods: Background
  2. Panel Data Analysis Part I: Classical Methods: Background Material
  3. Panel Data Analysis Part II: Feasible Estimators: Background
  4. Panel Data Analysis Part III: Modern Moment Estimation
• Cross Section Bias: Age, Period and Cohort Effects
• Duration Models Introduction to Single Spell Models
• Multistate Duration Models
• Sampling Plans and Initial Condition Problems For Continuous Time Duration Models

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Supplemental Reading

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Randomization

• Randomized Evaluations,  excerpt from Econometric Evaluation of Social Programs, Part II
• Some Problems with Experiments
• (*)Heckman, James J. (2020). “Randomization and Social Policy Evaluation Revisited.” In: Randomized Control Trials in the Field of Development: A Critical Perspective. F. Bédécarrats, I. Guérin, and F. Roubaud, eds. New York, NY: Oxford University Press.
• Kline, Patrick, and Christopher R. Walters. (2016). “Evaluating Public Programs with Close Substitutes: The Case of Head Start.” The Quarterly Journal of Economics, 131(4):1795-1848.
  • Evaluating Public Programs with Close Substitutes: The Case of Head Start
• (*)Banerjee, A. V., and E. Duflo. (2017). “An Introduction to the “Handbook of Field Experiments”.” In Handbook of Economic Field Experiments, edited by Abhijit Vinayak Banerjee and Esther Duflo. Chapter 1, pp. 1-24. Amsterdam: North-Holland.
• Deaton, Angus. 2010. “Instruments, Randomization, and Learning about Development.” Journal of Economic Literature, 48(2):424-455.
• Abadie, Alberto, Susan Athey, Guido W. Imbens, and Jeffrey M. Wooldridge. (2020). “Sampling-Based versus Design-Based Uncertainty in Regression Analysis.” Econometrica, 88(1):265-296.
  • Sampling based vs. Design based Uncertainty in Regression Analysis,  Abadie, Athey, Imbens, and Wooldridge (2019)
• Drèze, Jean. 2022. “On the Perils of Embedded Experiments.” [Blog Post]. Ideas for India for More Evidence-Based Policy, accessed March 14, 2022. https://www.ideasforindia.in/topics/miscellany/on-the-perils-of-embedded-experiments.html.
• Randomized Control Trials in the Field of Development: A Critical Perspective. F. Bédécarrats, I. Guérin, and F. Roubaud, eds. New York, NY: Oxford University Press.

Topic 1. Causality

• Abadie, Alberto, and Matias D. Cattaneo. (2018). “Econometric Methods for Program Evaluation.” Annual Review of Economics, 10(1):465-503.
• Angrist, Joshua D., Guido W. Imbens, and Donald B. Rubin. (1996). “Identification of Causal Effects Using Instrumental Variables.” Journal of the American Statistical Association, 91(434):444-455.
• Athey, Susan, and Guido W. Imbens. (2017). “The State of Applied Econometrics: Causality and Policy Evaluation.” Journal of Economic Perspectives, 31(2):3-32.
• Blundell, Richard, and Monica Costa Dias. (2009). “Alternative Approaches to Evaluation in Empirical Microeconomics.” The Journal of Human Resources, 44(3):565-640.
• Bollen, Kenneth A., and Judea Pearl. 2013. “Eight Myths About Causality and Structural Equation Models.” In Handbook of Causal Analysis for Social Research, edited by Stephen L. Morgan, 301-328. Dordrecht: Springer Netherlands.
• Chalak, Karim, and Halbert White. (2011). “Viewpoint: An extended class of instrumental variables for the estimation of causal effects.” The Canadian Journal of Economics, 44(1):1-51.
• Freedman, David, David Collier, Jasjeet Singh Sekhon, and Philip B. Stark. (2010). Statistical Models and Causal Inference: A Dialogue With the Social Sciences. New York: Cambridge University Press.
• Bjerkholt, Olav, and Duo Qin. (2011). A Dynamic Approach to Economic Theory: Lectures By Ragnar Frisch At Yale University. New York: Routledge.
• Holland, Paul W. (1986). “Statistics and Causal Inference.” Journal of the American Statistical Association, 81(396):945-960.
• Jaber, Amin, Jiji Zhang, and Elias Bareinboim. (2019). “Causal Identification under Markov Equivalence: Completeness Results.” In Proceedings of the 36th International Conference on Machine Learning, Volume 97, edited by Chaudhuri Kamalika and Salakhutdinov Ruslan, pp. 2981-2989. Long Beach, CA: MLR Press.
• Matzkin, Rosa L. (2005). “Identification of consumers’ preferences when their choices are unobservable.” Economic Theory, 26(2):423-443.
• Matzkin, Rosa L. (2013). “Nonparametric Identification in Structural Economic Models.” Annual Review of Economics, 5(1):457-486.
• Newey, Whitney K., and Daniel McFadden. (1994). “Large sample estimation and hypothesis testing.” In Handbook of Econometrics, 2111-2245. Elsevier.
• Pearl, Judea. (1995). “Causal Diagrams for Empirical Research.” Biometrika, 82(4):669-688.
• Pearl, Judea. (2009). “Causal inference in statistics: An overview.” Statistics Surveys, 3:96-146.
• White, Halbert, and Karim Chalak. (2013). “Identification and Identification Failure for Treatment Effects Using Structural Systems.” Econometric Reviews, 32(3):273-317.

Topic 2. Learning from Data

• Misak, C. J. (2004). Truth and the end of inquiry: A Peircean account of truth. New York: Clarendon Press ; Oxford University Press.

Topic 3: Discrete Choice and Self-Selection

• Carneiro, Pedro, James J. Heckman, and Edward Vytlacil. (2010). “Evaluating Marginal Policy Changes and the Average Effect of Treatment for Individuals at the Margin.” Econometrica, 78(1):377-394.
• Heckman, J. J. and Honoré, B. (1990). “The Empirical Content of the Roy Model.” Econometrica, 58(5): 1121-1149.
• Iskhakov, Fedor, John Rust, and Bertel Schjerning. (2020). “Machine Learning and Structural Econometrics: Comparisons and Contrasts.” Georgetown University, Department of Economics. Unpublished manuscript.
• Choice under Uncertainty: Expected Utility, Heckman and Jagelka (2019)
• Bollen and Pearl

Topic 5. Instrumental Variables

Topic 6. Matching

• Glewwe, Paul, and Petra Todd. 2022. Impact Evaluation in International Development : Theory, Methods and Practice. Washington, DC: World Bank.

Topic 7. General Principles Underlying All Econometric Estimators

• Heckman, James J. (2008). “The Principles Underlying Evaluation Estimators with an Application to Matching.” Annales d”Economie et de Statistique, (91/92):9-73.

Topic 8: Simultaneous Equations and Social Interactions

 

Topic 9: Longitudinal and Panel Data

• Heckman, James J., and Richard Robb. (1985). “Alternative Methods for Evaluating the Impact of Interventions.” In Longitudinal Analysis of Labor Market Data, edited by James J. Heckman and Burton S. Singer, 156-245. Cambridge University Press.

Social Experiments

• Todd, Petra E., and Kenneth I. Wolpin. 2006. “Assessing the Impact of a School Subsidy Program in Mexico: Using a Social Experiment to Validate a Dynamic Behavioral Model of Child Schooling and Fertility.” American Economic Review, 96(5):1384-1417.
• Moffatt, Peter G. (2021). Experimetrics: A Survey. In Foundations and Trends® in Econometrics, 11(1–2):1-152.
• Kitagawa, Toru, and Aleksey Tetenov. (2018). “Who Should Be Treated? Empirical Welfare Maximization Methods for Treatment Choice.” Econometrica, 86(2):591-616.

Causal Analysis and Structural Analysis

Discrete Choice

• Ben-Akiva, Moshe, Daniel McFadden, and Kenneth Train. (2019). “Foundations of Stated Preference Elicitation: Consumer Behavior and Choice-based Conjoint Analysis.” Foundations and Trends in Econometrics, 10(1-2):1-144.

Supplemental Slides

• The Dynamics of Productivity in the Telecommunications Equipment Industry, Olley and Pakes (1996)
• The Empirical Importance of Bundling A Test of the Hypothesis of Equal Factor Price Across All Sectors (From Heckman and Scheinkman, Review of Economic Studies 54(2), 1987)
• Factor Models: A Review
• ITT: Randomize Eligibility
• Duration Models: Introduction to Single Spell Models
• Example

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