Lecture 2: Thursday, March 23, 2023

1. Slides from March 21, 2023
   1. Learning from Data
   2. Abducting Economics: How to Learn from Surprises, Heckman and Singer (2016)
   3. Friedman’s Approach to Empirical Economics
   4. (*)  Hypothesis Testing: Part I
   5. Econometric Approach to Causality, Heckman and Pinto, 2022
   6. Econometric Policy Analysis
2. Causality and Econometrics, Heckman and Pinto, 2022
3. What is a causal effect? How to express it? And why it matters, Heckman and Pinto
4. Econometric Causality
5. Causality and Econometrics: Part I by Heckman and Pinto (2022)
6. Causality and Econometrics: Part II by Heckman and Pinto (2022)
7. Causality in Econometrics and Statistics: Structural Models are Causal Models Some Formal Statements Part III on Causality, Heckman and Pinto
8. Causality in Econometrics and Statistics: Structural Models are Causal Models (Do-Calculus Extract), Heckman and Pinto
9. Abduction, Singer (2008)
10. Continuous Versus Episodic Change: The Impact of Civil Rights Policy on the Economic Status of Blacks, Donohue and Heckman (1991)
11. The Causes and Consequences of Self-Employment over the Life Cycle, Humphries (2019).
12. Transparency, Reproducibility, and the Credibility of Economics Research, Christensen and Miguel (2018)
13. Determining the Impact of Federal Antidiscrimination Policy on the Economic Status of Blacks: A Study of South Carolina, Heckman and Payner (1989).

Lecture 3: Tuesday, March 28, 2023

1. Styles of Empirical Research
2. A Running Example Based on: Alternative Methods For Evaluating the Impact of Interventions: An Overview
3. Econometric Approach to Causality, Heckman and Pinto, 2022
4. Madansky Method Based on Replacement Functions
5. Olley and Pakes, Econometrica (1996)
6. Theil Interpretation of Regression
7. The Theil-Sen Estimators in Linear Regression, Peng (2008)
8. Econometric Estimators as Weighting Schemes , Heckman, LaLonde, and Smith (1999)

Supplement

1. Alternative Methods For Evaluating the Impact of Interventions: An Overview, Heckman and Robb (1985)

Lecture 4: Thursday, March 30, 2023

1. Econometric Approach to Causality, Heckman and Pinto, 2022
2. Madansky Method Based on Replacement Functions
3. Factor Models: A Review
4. Olley and Pakes, Econometrica (1996)
5. Discrete Dependent Variable Models
6. Conditional Logit Models
7. Classical Discrete Choice Theory
8. Probabilistic Choice Models

Supplement

1. Alternative Methods For Evaluating the Impact of Interventions: An Overview,  Heckman and Robb (1985)

Lecture 5: Tuesday, April 4, 2023

1. Extract: Notes on Roy Models and Generalized Roy
2. Roy Models of Policy Evaluation
3. Conditional Logit Models
4. The Normal Generalized Roy Model
5. Notes on Identification of the Roy Model and the Generalized Roy Model
6. Theil Interpretation of Regression
7. Interpreting IV, Part 1
8. Interpreting IV: More On Roy Model
9. Interpreting IV: LATE
10. RDD
11. Interpreting IV: What Economic Questions Can LATE Answer?
12. ITT: Randomize Eligibility
13. MTE as Generator of All Treatment Effects: IV and Policy Weights
14. IV Weights and Yitzhaki’s Theorem
15. Some Evidence on the Returns to Schooling, Carneiro, Heckman and Vytlacil (2011)
16. What Does IV Estimate? Carneiro, Heckman and Vytlacil (2011)
17. Comparing IV With Explicitly Formulated Economic Structural Models: What Simple IV Can and Cannot Identify

Supplement

1. Probabilistic Choice Models
2. The Theil-Sen Estimators in Linear Regression, Peng (2008)
3. Matching as an Econometric Evaluation Estimator, Heckman, Ichimura, and Todd (1998)

Lecture 6: Thursday, April 6, 2023

1. The Normal Generalized Roy Model
2. Notes on Identification of the Roy Model and the Generalized Roy Model
3. Interpreting IV, Part 1
4. Interpreting IV: More On Roy Model
5. Interpreting IV: LATE
6. Interpreting IV: What Economic Questions Can LATE Answer?
7. Theil Interpretation of Regression
8. IV Weights and Yitzhaki’s Theorem
9. RDD
10. ITT: Randomize Eligibility
11. What Does IV Estimate? Carneiro, Heckman and Vytlacil (2011)
12. Comparing IV With Explicitly Formulated Economic Structural Models: What Simple IV Can and Cannot Identify
13. MTE as Generator of All Treatment Effects: IV and Policy Weights

Supplement

1. Probabilistic Choice Models
2. The Theil-Sen Estimators in Linear Regression, Peng (2008)
3. Matching as an Econometric Evaluation Estimator, Heckman, Ichimura, and Todd (1998)
4. Some Evidence on the Returns to Schooling, Carneiro, Heckman and Vytlacil (2011)

Lecture 7: Tuesday, April 11, 2023

Review from April 7, 2023

• The Normal Generalized Roy Model
• Interpreting IV, Part 1
• Interpreting IV: More On Roy Model

Slides for April 11, 2023

1. Notes on Identification of the Roy Model and the Generalized Roy Model
2. Interpreting IV: LATE
3. Interpreting IV: What Economic Questions Can LATE Answer?
4. Theil Interpretation of Regression
5. IV Weights and Yitzhaki’s Theorem
6. RDD
7. ITT: Randomize Eligibility
8. What Does IV Estimate? Carneiro, Heckman and Vytlacil (2011)
9. Comparing IV With Explicitly Formulated Economic Structural Models: What Simple IV Can and Cannot Identify
10. MTE as Generator of All Treatment Effects: IV and Policy Weights
11. Characterizing Selection Bias Using Experimental Data, Heckman, Ichimura, Smith, and Todd (1998)
12. 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.
13. Econometric Estimators as Weighting Schemes , Heckman, LaLonde, and Smith (1999)

Supplement

1. Probabilistic Choice Models
2. The Theil-Sen Estimators in Linear Regression, Peng (2008)
3. Some Evidence on the Returns to Schooling, Carneiro, Heckman and Vytlacil (2011)
4. Matching As An Econometric Evaluation Estimator, Heckman, Ichimura, and Todd (1998)

Lecture 8: Thursday, April 13, 2023

1. Understanding Instrumental Variables in Models with Essential Heterogeneity, James Heckman, Sergio Urzua and Edward Vytlacil
2. IV Weights and Yitzhaki’s Theorem
3. RDD
4. ITT: Randomize Eligibility
5. What Does IV Estimate? Carneiro, Heckman and Vytlacil (2011)
6. Comparing IV With Explicitly Formulated Economic Structural Models: What Simple IV Can and Cannot Identify
7. MTE as Generator of All Treatment Effects: IV and Policy Weights
8. 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.
9. Characterizing Selection Bias Using Experimental Data, Heckman, Ichimura, Smith, and Todd (1998)
10. Econometric Estimators as Weighting Schemes , Heckman, LaLonde, and Smith (1999)
11. Ability Bias, Errors in Variables and Sibling Methods: Background
12. Panel Data Analysis Part I – Classical Methods: Background Material
13. Panel Data Analysis Part III – Modern Moment Estimation
14. Cross Section Bias: Age, Period and Cohort Effects

Supplement

Review from April 7, 2023

• The Normal Generalized Roy Model
• Interpreting IV, Part 1
• Interpreting IV: More On Roy Model

Slides for April 11, 2023

1. Notes on Identification of the Roy Model and the Generalized Roy Model
2. Interpreting IV: LATE
3. Interpreting IV: What Economic Questions Can LATE Answer?
4. Theil Interpretation of Regression

Lecture 9: Tuesday, April 18, 2023

1. Understanding Instrumental Variables in Models with Essential Heterogeneity, James Heckman, Sergio Urzua and Edward Vytlacil
2. RDD
3. Interpreting IV: What Economic Questions Can LATE Answer?
4. Cost Benefit Analysis Using the MTE, Heckman and Vytlacil (2004)
5. What Does IV Estimate? Carneiro, Heckman and Vytlacil (2011)
6. Comparing IV With Explicitly Formulated Economic Structural Models: What Simple IV Can and Cannot Identify
7. Characterizing Selection Bias Using Experimental Data, Heckman, Ichimura, Smith, and Todd (1998)
8. Econometric Estimators as Weighting Schemes , Heckman, LaLonde, and Smith (1999)
9. The Economics and Econometrics of Active Labor Market Programs: Generalized Differences Estimators, Heckman, LaLonde, and Smith
10. 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.

Supplement

1. ITT: Randomize Eligibility
2. MTE as Generator of All Treatment Effects: IV and Policy Weights
3. Ability Bias, Errors in Variables and Sibling Methods: Background
4. Panel Data Analysis Part I – Classical Methods: Background Material
5. Panel Data Analysis Part III – Modern Moment Estimation
6. Cross Section Bias: Age, Period and Cohort Effects
7. The Normal Generalized Roy Model
8. Interpreting IV, Part 1
9. Interpreting IV: More On Roy Model
10. Notes on Identification of the Roy Model and the Generalized Roy Model
11. Interpreting IV: LATE
12. Theil Interpretation of Regression
13. Duration Models Introduction to Single Spell Models
14. The Identification Zoo: Meanings of Identification in Econometrics
15. Sampling Plans and Initial Condition Problems for Continuous Time Duration Models
16. Multistate Duration Models

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Lecture Note Supplement

•  Econometric Evaluation of Social Programs Part II, Heckman and Vytlacil (2007)
• Definition of Samples
• The Pre-Test Estimator by Heckman and Pinto [needs retitle]
• Skills and Tasks in the Labor Market, Heckman and Sedlacek (1985) [needs retitle]
• Shadow Prices, Market Wages and Labor Supply by Heckman (1974) [needs retitle]
• Rate of Return Continuation Values and Option Values in a Simple Dynamic Model , Eisenhauer, Heckman and Mosso (2015). [needs retitle]
• Separating Heterogeneity from Uncertainty Decomposing Trends in Inequality in Earnings into Forecastable and Uncertain Components Extract [needs retitle]
• How To Correct for Sampling Biases [needs retitle]
• Extract from: Simulation Study of the Sensitivity of Nonexperimental Methods to Matching and Alternative Assumptions
• Derivation of Other Weights
• Alternative Methods for Evaluating the Impact of Interventions: An Overview, Heckman and Robb (1985)
• Panel Data Models: Some Recent Developments
• Modeling the Income Process [needs retitle]
• Factor Models: A Review [needs retitle]
• Factor Models [needs retitle]
• Notes on Factor Models and the Hicks Lecture Model with Normal Random Variables [needs retitle]
• Panel Data Analysis Part II: Feasible Estimators: Background [needs retitle]