Lecture 2, Tuesday, May 4, 2021

1. Hypothesis Testing, Part 1
2. The Pre-Test Estimator Heckman and Pinto
3. Abducting Economics: How to Learn from Surprises, Heckman and Singer (2016)
4. The Causes and Consequences of Self-Employment over the Life Cycle, Humphries (2019).
5. Friedman’s Approach to Empirical Economics
6. Continuous Versus Episodic Change…, Donohue and Heckman (1991)
7. Classical Discrete Choice Theory
8. Notes on Roy Models and Generalized Roy Models
9. Roy Models and Policy Evaluation
10. The Normal Generalized Roy Model
11. Notes on Identification of the Roy Model and the Generalized Roy Model
12. Definition of Samples
13. How To Correct for Sampling Biases
14. Shadow Prices, Market Wages, and Labor Supply

Lecture 3, Thursday, May 6, 2021

1. Abducting Economics: How to Learn from Surprises, Heckman and Singer (2016)
2. The Causes and Consequences of Self-Employment over the Life Cycle, Humphries (2019).
3. Friedman’s Approach to Empirical Economics
4. Continuous Versus Episodic Change…, Donohue and Heckman (1991)
5. Classical Discrete Choice Theory
6. Notes on Roy Models and Generalized Roy Models
7. Roy Models and Policy Evaluation
8. The Normal Generalized Roy Model
9. Notes on Identification of the Roy Model and the Generalized Roy Model
10. Shadow Prices, Market Wages, and Labor Supply
11. Gender, Selection into Employment, and the Wage Impact of Immigration, Borjas and Edo (2021)
12. Definition of Samples
13. How To Correct for Sampling Biases

Lecture 4, Tuesday, May 11, 2021

Review

1. Friedman’s Approach to Empirical Economics
2. Classical Discrete Choice Theory
3. Roy Model
   1. Notes on Roy Models and Generalized Roy Models
   2. Roy Models and Policy Evaluation
   3. The Normal Generalized Roy Model
   4. Notes on Identification of the Roy Model and the Generalized Roy Model

Slides

1. Definition of Samples
2. How To Correct for Sampling Biases
3. Shadow Prices, Market Wages, and Labor Supply
4. What is a Causal Effect? How to Express It? And Why it Matters
5. Causality Part II: Further Comments

Lecture 5, Thursday, May 13, 2021

1. Definition of Samples
2. How To Correct for Sampling Biases
3. Shadow Prices, Market Wages, and Labor Supply
4. What is a Causal Effect? How to Express It? And Why it Matters
5. Causality Part II: Further Comments
6. 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.

Lecture 6, Tuesday, May 18, 2021

1. What is a Causal Effect? How to Express It? And Why it Matters
2. Causality Part II: Further Comments
3. 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.
4. Randomized Evaluations from Econometric Evaluation of Social Programs, Part II
5. Some Problems with Experiments

Lecture 7, Thursday, May 20, 2021

1. 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.
2. Randomized Evaluations from Econometric Evaluation of Social Programs, Part II
3. Some Problems with Experiments

Background

1. What is a Causal Effect? How to Express It? And Why it Matters
2. Causality Part II: Further Comments

Lecture 8, Tuesday, May 25, 2021

1. Revised Yitzhaki
2. Yitzhaki Weights Examples
3. Interpreting IV, What Economic Questions Can LATE Answer?
4. Interpreting IV: What Does IV Estimate?
5. Comparing IV with Structural Models: What Simple IV Can and Cannot Identify
6. Ability Bias, Errors in Variables and Sibling Methods: Background
7. Panel Data Analysis Part I: Classical Methods: Background Material
8. Panel Data Analysis Part II: Additional Results
9. Panel Data Analysis Part III: Modern Moment Estimation
10. Modeling the Income Process
11. Factor Models: A Review
12. Factor Models
13. Cross Section Bias: Age, Period and Cohort Effects
14. Separating Heterogeneity from Uncertainty Decomposing Trends in Inequality in Earnings into Forecastable and Uncertain Components Extract

Supplement

1. IV Weights and Yitzhaki’s Theorem
2. Yitzhaki Weights: Beta Example
3. Yitzhaki Derived the Weights Used by the Proponents of LATE but Without Citation: The Weights Have a Lot of Intuition
4. Interpreting IV, Part 1

Lecture 9, Thursday, May 27, 2021

1. Interpreting IV, What Economic Questions Can LATE Answer?
2. Examples of Partial Identification of MTE
3. Interpreting IV: What Does IV Estimate?
4. The Economics and Econometrics of Active Labor Market Programs: Generalized Differences Estimators, Heckman, LaLonde, and Smith
5. Modeling the Income Process
6. Panel Data Analysis Part III: Modern Moment Estimation
7. Cross Section Bias: Age, Period and Cohort Effects
8. Factor Models
9. Factor Models: A Review
10. Duration Models Introduction to Single Spell Models
11. Sampling Plans and Initial Condition Problems For Continuous Time Duration Models
12. Multistate Duration Models

Slides Not Used

• Econometric Causality: Part I on Causality
  1. Full version: Econometric Causality
• Causal Frameworks for Complex Causal Models
• Causality in Econometrics and Statistics: Structural Models are Causal Models Formal Statements Part III on Causality
• Field Experiments and the Practice of Policy, Duflo (2020)
• Econometric Approach to Causality, SAMSI Keynote Address (2021)
• Alternative Model Based and Design Based Frameworks…, Sterba (2009)
• Sampling based vs. Design based Uncertainty in Regression Analysis, Abadie, Athey, Imbens, and Wooldridge (2019)
• Nonparametric Identification, Matzkin (2007)
• Skills and Tasks in the Labor Market
• The Identification Zoo: Meanings of Identification in Econometrics, Lewbel (2019)
• Alternative Methods for Evaluating the Impact of Interventions: An Overview, Heckman and Robb (1985)
• Notes on Factor Models and the Hicks Lecture Model with Normal Random Variables
• Interpreting IV, More on Roy Model
• Interpreting IV, LATE
• RDD
• Using Matching, Instrumental Variables and Control Functions to Estimate Economic Choice Models , Heckman and Navarro (2004)
• Characterizing Selection Bias Using Experimental Data, Heckman, Ichimura, Smith, and Todd (1998)
• Some Mechanics on the Method of Matching,  Heckman, LaLonde, and Smith (1999)
• Matching As An Econometric Evaluation Estimator,, Heckman, Ichimura, and Todd (1997)
• Econometric Evaluation of Social Programs Part II
• Derivation of Other Weights
• Some Evidence on the Returns to Schooling, Carneiro, Heckman and Vytlacil (2011)
• Evaluating Public Programs with Close Substitutes: The Case of Head Start , Kline and Walters (2016)
• Panel Data Models: Some Recent Developments, Arellano and Honore (2001)
• Rate of Return Continuation Values and Option Values in a Simple Dynamic Model

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2020 Slides by Lecture Date

Lecture 1, May 9, 2020

1. Goals of the Course
2. General Themes of Econ 312
3. Learning from Data
4. Abducting Economics: How to Learn from Surprises, Heckman and Singer (2016)
5. Abduction, Singer (2008)
6. Hypothesis Testing, Part 1
7. The Causes and Consequences of Self-Employment over the Life Cycle, Humphries (2019).

Lecture 2, May 12, 2020

1. Hypothesis Testing, Part 1
2. The Causes and Consequences of Self-Employment over the Life Cycle, Humphries (2019).
3. Monologue on Causality
4. Econometric Causality: Part I on Causality
5. Definition of Samples
6. Classical Discrete Choice Theory
7. How To Correct for Sampling Biases
8. Sampling Plans and Initial Condition Problems For Continuous Time Duration Models

Lecture 3, May 14, 2020

1. Monologue on Causality
2. Econometric Causality: Part I on Causality
3. Classical Discrete Choice Theory
4. Definition of Samples
5. Sampling Plans and Initial Condition Problems For Continuous Time Duration Model
6. How To Correct for Sampling Biases

Roy Model and Generalized Roy Model to Interpret Evidence

1. The Roy Model and Policy Evaluation
2. Interpreting IV, Part 1
3. Interpreting IV, More on Roy Model
4. Interpreting IV, LATE
5. RDD
6. Interpreting IV, What Economic Questions Can LATE Answer?
7. Interpreting IV: What Does IV Estimate?
8. The Normal Generalized Roy Model
9. Some Evidence on the Returns to Schooling, Carneiro, Heckman and Vytlacil (2011)
10. Notes on Identification of the Roy Model and the Generalized Roy Model

Lecture 4, May 19, 2020

Review

1. Classical Discrete Choice Theory
2. Definition of Samples
3. How To Correct for Sampling Biases
4. Sampling Plans and Initial Condition Problems For Continuous Time Duration Models

Main Class

1. Notes on Roy Models and Generalized Roy
2. The Roy Model and Policy Evaluation
3. The Normal Generalized Roy Model
4. Notes on Identification of the Roy Model and the Generalized Roy Model
5. Interpreting IV, LATE
6. Interpreting IV, More on Roy Model
7. MTE as Generator of All Treatment Effects: IV and Policy Weights
8. Interpreting IV, What Economic Questions Can LATE Answer?
9. Interpreting IV: What Does IV Estimate?
10. Some Evidence on the Returns to Schooling, Carneiro, Heckman and Vytlacil (2011)
11. RDD

Supplement

• Interpreting IV, Part 1
• Matching As An Econometric Evaluation Estimator. Heckman, Ichimura, and Todd (1997)

Lecture 5, May 21, 2020

1. MTE as Generator of All Treatment Effects: IV and Policy Weights
2.  IV Weights and Yitzhaki’s Theorem
3. Yitzhaki Derived the Weights Used by the Proponents of LATE but Without Citation: The Weights Have a Lot of Intuition
4. Derivation of Other Weights
5. Interpreting IV, What Economic Questions Can LATE Answer?
6. Interpreting IV: What Does IV Estimate?
7. Some Evidence on the Returns to Schooling, Carneiro, Heckman and Vytlacil (2011)
8. RDD
9. Randomized Evaluations from Econometric Evaluation of Social Programs, Part II

Supplement

1. Notes on Roy Models and Generalized Roy
2. The Roy Model and Policy Evaluation
3. The Normal Generalized Roy Model
4. Notes on Identification of the Roy Model and the Generalized Roy Model
5. Interpreting IV, LATE
6. Interpreting IV, More on Roy Model

Lecture 6, May 26, 2020

1. Interpreting IV, What Economic Questions Can LATE Answer?
2. Interpreting IV: What Does IV Estimate?
3. Comparing IV with Structural Models: What Simple IV Can and Cannot Identify
4. Some Evidence on the Returns to Schooling, Carneiro, Heckman and Vytlacil (2011)
5. RDD
6. Some Mechanics on the Method of Matching, Heckman, LaLonde, and Smith (1999)
7. Yitzhaki Derived the Weights Used by the Proponents of LATE: The Weights Have a Lot of Intuition
8. Using Matching, Instrumental Variables and Control Functions to Estimate Economic Choice Models, Heckman and Navarro (2006)
9. Characterizing Selection Bias Using Experimental Data, Heckman, Ichimura, Smith, and Todd (1998)
10. Randomized Evaluations from Econometric Evaluation of Social Programs, Part II

Supplement

1. MTE as Generator of All Treatment Effects: IV and Policy Weights
2.  IV Weights and Yitzhaki’s Theorem
3. Derivation of Other Weights
4. Matching As An Econometric Evaluation Estimator. Heckman, Ichimura, and Todd (1997)

Lecture 7, May 28, 2020

1. Some Mechanics on the Method of Matching, Heckman, LaLonde, and Smith (1999)
2. Using Matching, Instrumental Variables and Control Functions to Estimate Economic Choice Models, Heckman and Navarro (2006)
3. Characterizing Selection Bias Using Experimental Data, Heckman, Ichimura, Smith, and Todd (1998)
4. 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.
5. Randomized Evaluations from Econometric Evaluation of Social Programs, Part II
6. Simultaneous Causality: Part IV on Causality
7. Discrete Time Panel Data Methods
   1. Ability Bias, Errors in Variables and Sibling Methods
   2. Panel Data, Part 1: Classical Methods
   3. Panel Data, Part 2: Feasible Estimators
   4. Panel Data, Part 3: Modern Moment Estimation
8. Cross Section Bias: Age Period and Cohort Effects
9. Alternative Methods for Evaluating the Impact of Interventions: An Overview, Heckman and Robb (1985)

Supplement

• 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

Lecture 8, June 2, 2020

1. 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.
   • The Principles Underlying Evaluation Estimators: Appendix
2. ITT: Randomize Eligibility
3. Factor Models: A Review
4. 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)
5. The Dynamics of Productivity in the Telecommunications Equipment Industry, Olley and Pakes (1996)
6. Randomized Evaluations from Econometric Evaluation of Social Programs, Part II
7. Simultaneous Causality: Part IV on Causality
8. Ability Bias, Errors in Variables and Sibling Methods
9. Panel Data, Part 1: Classical Methods
10. Panel Data, Part 2: Feasible Estimators
11. Panel Data, Part 3: Modern Moment Estimation
12. Alternative Methods for Evaluating the Impact of Interventions: An Overview, Heckman and Robb (1985)
13. Duration Models: Introduction to Single Spell Models

Supplement

• Cross Section Bias: Age Period and Cohort Effects

Lecture 9, June 4, 2020

1. 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.
2. Some Mechanics on the Method of Matching, Heckman, LaLonde, and Smith (1999)
3. Yitzhaki Derived the Weights Used by the Proponents of LATE: The Weights Have a Lot of Intuition
4. Ability Bias, Errors in Variables and Sibling Methods
5. Panel Data, Part 1: Classical Methods
6. Panel Data, Part 3: Modern Moment Estimation
7. Alternative Methods for Evaluating the Impact of Interventions: An Overview, Heckman and Robb (1985)
8. Duration Models: Introduction to Single Spell Models
9. Example

Supplement

• Cross Section Bias: Age Period and Cohort Effects