B2154 - MICROECONOMETRICS

Learning outcomes

At the end of the course the student has acquired knowledge of the core microeconometric models and methods designed to study the behaviour of economic agents using cross-section and panel data, including static paneld data models, instrumental variable methods, and the most widely used limited dependent variable modes. In particular, he/she is able: - to critically understand the applications of these models in the recent empirical economic literature; - to apply the models and perform his/her own analysis of economic datasets using the software STATA.

Course contents

Part 1) Introduction: scope and toolbox of microeconometrics

 

Part 2) A framework to define causality

 

Part 3) A recap: the Population Regression Function

3.1 What is the Population Regression Function (PRF) and why are we interested in it

3.2 Sample Regression Function (SRF) and estimation of the PRF

3.2.1 Methods of Moments

3.2.2 Ordinary Least Squares

3.2.3 Maximum Likelihood

3.3 Properties of estimators

3.3.1 Finite sample properties: unbiasedness

3.3.2 Finite sample properties: efficiency

3.3.3 Asymptotic properties: consistency

3.3.4 Asymptotic distribution of an estimator and asymptotic efficiency

3.3.5 Focus on the properties of Maximum Likelihood Estimators

 

Part 4) Population Regression Function and Causality

4.1. Can the Population Regression function be interpreted causally?

4.1.1 A causal Data Generating Process

4.1.2 The PRF, the ATE and the ATT

4.2. Can adding controls help give a causal interpretation to the PRF?

4.2.1 The Conditional Indepedence Assumption

4.2.2 The Population Multiple Regression Function (PMRF, a recap)

4.2.2 The PMRF under the CIA

4.3. Good and bad habits concerning control variables

 

Part 5) Panel data: Fixed and Random effects estimators

5.1 What are panel data?

5.2 Fixed effects (within) estimation of the PRF with panel data

5.2.1 A recap of Projection and Partialling Out matrices

5.2.2 Partialling Out and the “within” transformation

5.2.3 First differences and Fixed Effects estimation

5.2.4 Panel data and measurement error

5.3 Between Estimation

5.4 Random Effect Estimation

5.5 Mundlak (1978): a reconciliation of fixed and random effects estimation

5.6 The Hausman Specification Test for Random effects

 

Part 6) Panel data: Difference in Difference estimation and Synthetic Control Methods

6.1 At the origin of Difference in Difference estimation

6.2 Beyond the 2 × 2 Difference in Difference setting

6.2.1 The Two-Way Fixed Effect Model: TWFE

6.2.2 Event-Study designs

6.2.3 The solution proposed by Callaway and Sant’Anna (2021)

6.2.4 Comparing techniques in an Event-study design

6.3 Synthetic Control Methods

6.4 Synthetic Difference in Differences

 

Part 7) Control functions and the pre-1990 approach to Instrumental Variable estimation

7.1 Heckman’s solution for endogenous dummy variable models

7.2 Pre-1990 approach to Instrumental Variable Estimation

7.3 Control functions

 

Part 8) The Angrist-Imbens-Rubin (AIR) interpretation of IV methods

8.1 Starting from scratch: what do we need to identify a causal effect

8.2 Assumptions of the Angrist-Imbens-Rubin causal model

8.3 The Local Average Treatment Effect

 

Part 9) Selection on observable and matching strategies

9.1 The setting and notation

9.2 Matching and regression

9.3 Propensity score matching

9.3.1 Steps to implement Propensity Score Matching

9.3.2 Estimation of the Propensity Score

9.3.3 Estimators of the ATT based on the Propensity Score

 

Part 10) An introduction to non-standard dependent variables

10.1 Binary outcomes

10.1.1 The Linear Probability Model

10.1.2 Non-Linear Probability Models

10.2 Multinomial outcomes

10.2.1 The logistic distributional assumption

10.2.2 Multinomial Logit with decision-maker attributes onlys

10.2.3 Multinomial Logit with choice-specific attributes only

10.2.4 Hausman-type test for the IIA hypothesis

10.3 Count outcomes

10.4 Duration outcomes

Readings/Bibliography

PART 1) Introduction: scope and toolbox of microeconometrics

Athey, Susan, and Guido W. Imbens. 2017. “The State of Applied Econometrics: Causality and Policy Evaluation.” Journal of Economic Perspectives, 31(2): 3–32.

Kleinberg, Jon, Jens Ludwig, Sendhil Mullainathan, and Ziad Obermeyer. 2015. “Prediction Policy Problems.” American Economic Review: Papers & Proceedings, 105(5): 491–495.

 

PART 2) A framework to define causality

Holland, Paul W. 1986. “Statistics and Causal Inference.” Journal of the American Statistical Association, 81(396): 945–960.

LaLonde, Robert J. 1986. “Evaluating the Econometric Evaluations of Training Programs with Experimental Data.” American Economic Review, 76(4): 604–620.

Rubin, Donald B. 1974. “Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies.” Journal of Educational Psychology, 66(5): 688–701.

 

PART 3) A recap: the Population Regression Function

Angrist, Joshua D., and J¨orn-Steffen Pischke. 2009. Mostly Harmless Econometrics: An Empiricist’s Companion. First ed., Princeton, NJ:Princeton University Press.

Wooldridge, Jeffrey M. 2009. Introductory Econometrics: A Modern Approach. South-Western / Cengage Learning.

 

PART 4) Population Regression Function and Causality

Angrist, Joshua D., and J¨orn-Steffen Pischke. 2009. Mostly Harmless Econometrics: An Empiricist’s Companion. First ed., Princeton, NJ:Princeton University Press.

Black, Dan A., Jeffrey A. Smith, Mark C. Berger, and Brett J. Noel. 2003. “Is the Threat of Reemployment Services

More Effective Than the Services Themselves? Evidence from Random Assignment in the UI System.” American Economic Review, 93(4): 1313–1327.

Heckman, James J. 1997. “Instrumental Variables: A Study of Implicit Behavioral Assumptions Used in Making Program Evaluations.” Journal of Human Resources, 32(3): 441–462.

Heckman, James J., Sergio Urzua, and Edward Vytlacil. 2006. “Understanding Instrumental Variables in Models with Essential Heterogeneity.” Review of Economics and Statistics, 88(3): 389–432.

 

PART 5) Panel data: Fixed and Random effects estimators

Ashenfelter, Orley, and Alan B. Krueger. 1994. “Estimates of the economic return to schooling from a new sample of twins.” American Economic Review, 84(5): 1157–1173.

Balestra, Pietro, and Marc Nerlove. 1966. “Pooling Cross Section and Time Series Data in the Estimation of a Dynamic Model: The Demand for Natural Gas.” Econometrica, 34(3): 585–612.

Hausman, Jerry A. 1978. “Specification Tests in Econometrics.” Econometrica, 46(6): 1251–1271.

Hausman, Jerry A., and William E. Taylor. 1981. “Panel Data and Unobservable Individual Effects.” Econometrica, 49(6): 1377–1398.

Ichino, Andrea, and Giovanni Maggi. 1999. “Work Environment and Individual Background: Explaining Regional Shirking

Differentials in a Large Italian Firm.” Quarterly Journal of Economics, 114(3): 1057–1090.

Maddala, G. S. 1971. “The Use of Variance Components Models in Pooling Cross Section and Time Series Data.” Econometrica, 39(2): 341–358.

Mundlak, Yair. 1961. “Empirical Production Function Free of Management Bias.” Journal of Farm Economics, 43(1): 44–56.

Mundlak, Yair. 1978. “On the Pooling of Time Series and Cross Section Data.” Econometrica, 46(1): 69–85.

 

PART 6) Difference in Difference estimation and Synthetic Control Methods

Abadie, Alberto, Alexis Diamond, and Jens Hainmueller. 2010. “Synthetic Control Methods for Comparative Case

Studies: Estimating the Effect of California’s Tobacco Control Program.” Journal of the American Statistical Association, 105(490): 493–505.

Abadie, Alberto, and Javier Gardeazabal. 2003. “The Economic Costs of Conflict: A Case Study of the Basque Country.” American Economic Review, 93(1): 113–132.

Abadie, Alberto, and J´er´emy L’Hour. 2021. “A Penalized Synthetic Control Estimator for Disaggregated Data.” Journal of the American Statistical Association, 116(536): 1817–1834.

Arkhangelsky, Dmitry, and Guido Imbens. 2024. “Causal models for longitudinal and panel data: a survey.” The Econometrics Journal, 27(3): C1–C61.

Arkhangelsky, Dmitry, Susan Athey, David A. Hirshberg, Guido W. Imbens, and Stefan Wager. 2021. “Synthetic Difference-in-Differences.” American Economic Review, 111(12): 4088–4118.

Athey, Susan, and Guido W. Imbens. 2021. “Design-based analysis in difference-in-differences settings with staggered adoption.” Journal of Econometrics, 225(2): 726–751.

Ben-Michael, Eli, Avi Feller, and Jesse Rothstein. 2021. “The Augmented Synthetic Control Method.” Journal of the American Statistical Association, 116(536): 1789–1803.

Borusyak, Kirill, Xavier Jaravel, and Jann Spiess. 2024. “Revisiting Event-Study Designs: Robust and Efficient Estimation.” Review of Economic Studies, 91(6): 3253–3285.

Callaway, Brantly, and Pedro H. C. Sant’Anna. 2021. “Difference-in-differences with multiple time periods.” Journal of Econometrics, 225(2): 200–230.

Card, David, and Alan B. Krueger. 1994. “Minimum Wages and Employment: A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania.” American Economic Review, 84(4): 772–793.

de Chaisemartin, Clement, and Xavier d’Haultfœuille. 2020. “Two-Way Fixed Effects Estimators with Heterogeneous Treatment Effects.” American Economic Review, 110(9): 2964–2996.

Goodman-Bacon, Andrew. 2021. “Difference-in-Differences with Variation in Treatment Timing.” Journal of Econometrics, 225(2): 254–277.

Kahn-Lang, Ariella, and Kevin Lang. 2020. “The Promise and Pitfalls of Differences-in-Differences: Reflections on 16 and Pregnant and Other Applications.” Journal of Business & Economic Statistics, 38(3): 613–620.

Stevenson, Betsey, and Justin Wolfers. 2006. “Bargaining in the Shadow of the Law: Divorce Laws and Family Distress.” Quarterly Journal of Economics, 121(1): 267–288.

Sun, Liyang, and Sarah Abraham. 2021. “Estimating dynamic treatment effects in event studies with heterogeneous treatment effects.” Journal of Econometrics, 225(2): 175–199.

 

Part 7) Control functions and the pre-1990 approach to Instrumental Variable estimation

Coviello, Decio, Andrea Ichino, and Nicola Persico. 2015. “The Inefficiency of Worker Time Use.” Journal of the European Economic Association, 13(5): 906–947.

Hausman, Jerry A. 1978. “Specification Tests in Econometrics.” Econometrica, 46(6): 1251–1271.

Heckman, James J. 1978. “Dummy Endogenous Variables in a Simultaneous Equation System.” Econometrica, 46(4): 931–959.

Heckman, James J., and Richard Jr. Robb. 1985. “Alternative Methods for Evaluating the Impact of Interventions: An Overview.” Journal of Econometrics, 30(1-2): 239–267.

LaLonde, Robert J. 1986. “Evaluating the Econometric Evaluations of Training Programs with Experimental Data.” American Economic Review, 76(4): 604–620.

Maddala, G.S. 1983. Limited-Dependent and Qualitative Variables in Econometrics. Cambridge:Cambridge University Press.

Robinson, Chris, and Nigel Tomes. 1984. “Union Wage Differentials in the Public and Private Sectors: A Simultaneous Equations Specification.” Journal of Labor Economics, 2(1): 106–127.

Roy, A. D. 1951. “Some Thoughts on the Distribution of Earnings.” Oxford Economic Papers, 3(2): 135–146.

Willis, Robert J., and Sherwin Rosen. 1979. “Education and Self-Selection.” Journal of Political Economy, 87(5, Part2): S7–S36.

 

Part 8) The Angrist-Imbens-Rubin (AIR) interpretation of IV methods

Angrist, Joshua D. 1990. “Lifetime Earnings and the Vietnam Era Draft Lottery: Evidence from Social Security

Administrative Records.” American Economic Review, 80(3): 313–336.

Angrist, Joshua D., and Alan B. Krueger. 1991. “Does Compulsory School Attendance Affect Schooling and Earnings?” Quarterly Journal of Economics, 106(4): 979–1014.

Angrist, Joshua D., Guido W. Imbens, and Donald B. Rubin. 1996. “Identification of Causal Effects Using Instrumental

Variables (with comments).” Journal of the American Statistical Association, 91(434): 444–472.

Card, David. 1995. “Using Geographic Variation in College Proximity to Estimate the Return to Schooling.” In Aspects of Labour Market Behaviour: Essays in Honour of John Vanderkamp. , ed. L. N. Christofides, E. K. Grant and R.

Swidinsky, 201–222. Toronto:University of Toronto Press. Originally NBER Working Paper No. 4483 (1993).

Ichino, Andrea, and Rudolf Winter-Ebmer. 2004. “The Long-Run Educational Cost of World War II.” Journal of Labor Economics, 22(1): 57–86.

Scheiber, Noam. 2007. “Freaks and Geeks: How Freakonomics is Ruining the Dismal Science.” The New Republic, 236(12): 27–31. Published April 2, 2007.

Willis, Robert J., and Sherwin Rosen. 1979. “Education and Self-Selection.” Journal of Political Economy, 87(5, Part2): S7–S36.

 

Part 9) Selection on observable and matching strategies

Angrist, Joshua D. 1998. “Estimating the Labor Market Impact of Voluntary Military Service Using Social Security Data on Military Applicants.” Econometrica, 66(2): 249–288.

Becker, Sascha O., and Andrea Ichino. 2002. “Estimation of Average Treatment Effects Based on Propensity Scores.” The Stata Journal, 2(4): 358–377.

Dehejia, Rajeev H. 2005. “Practical propensity score matching: a reply to Smith and Todd.” Journal of Econometrics, 125(1-2): 355–364.

Dehejia, Rajeev H., and Sadek Wahba. 1999. “Causal Effects in Non-Experimental Studies: Reevaluating the Evaluation of Training Programs.” Journal of the American Statistical Association, 94(448): 1053–1062.

Ichino, Andrea, Fabrizia Mealli, and Tommaso Nannicini. 2008. “From Temporary Help Jobs to Permanent Employment: What Can We Learn from Matching Estimators and Their Sensitivity?” Journal of Applied Econometrics, 23(3): 305–327.

Ichino, Andrea, Guido Schwerdt, Rudolf Winter-Ebmer, and Josef Zweim¨uller. 2017. “Too Old to Work, Too Young to Retire?” Journal of the Economics of Ageing, 9(C): 14–29.

Nannicini, Tommaso. 2007. “Simulation-Based Sensitivity Analysis for Matching Estimators.” The Stata Journal, 7(3): 334–350.

Rosenbaum, Paul R., and Donald B. Rubin. 1983. “The Central Role of the Propensity Score in Observational Studies for Causal Effects.” Biometrika, 70(1): 41–55.

Smith, Jeffrey A., and Petra E. Todd. 2005a. “Does Matching Overcome LaLonde’s Critique of Nonexperimental Estimators?” Journal of Econometrics, 125(1-2): 305–353.

Smith, Jeffrey A., and Petra E. Todd. 2005b. “Rejoinder to “Does Matching Overcome LaLonde’s Critique of Nonexperimental Estimators?”.” Journal of Econometrics, 125(1-2): 365–375.

 

Part 10) An introduction to non-standard dependent variables

Greene, William H. 1997. Econometric Analysis. . 3rd ed., Upper Saddle River, NJ:Prentice Hall.

Hausman, Jerry A., and Daniel McFadden. 1984. “Specification Tests for the Multinomial Logit Model.” Econometrica, 52(5): 1219–1240.

 

Teaching methods

The course will feature 10 frontal lectures, approximately one for each part of the course.

 

Some problem sets will be distributed and will be corrected in review sessions by the Teaching Assistant of the course.

 

Andrea Ichino will be available for individual or group meetings in person or online to answer questions related to the course.

 

Assessment methods

Students will be evaluated with a written exam in class, of about 2.5 hours

 

Examples of the final exam questions will be distributed during the course.

 

Office hours

See the website of Andrea Carlo Michele Ichino