Master of Science in Economics

Updated A.Y. 2018-2019

Overview

The main aim of this course is to provide students with the necessary analytical tools to apply microeconometric methods to the analysis of economic data. The course is organized in three modules. The first module (Single-equation linear models) will familiarize students with the workhorse in empirical economics, namely the estimation of the linear regression model by Ordinary Least Squares (OLS) and Instrumental Variables (IV) estimators. The second module (Systems of linear equations) will introduce students to the estimation of linear system of equations, emphasizing the modern approach to system instrumental variables estimation based on the Generalized Method of Moments (GMM). Finally, the third module (Linear Unobserved-Effects Panel Data Models) will cover the econometric analysis of static linear panel data models. At the end of the course students should be able to specify the appropriate econometric model for investigating the problem under study and draw proper conclusions based on the results.

This course is taught at a level assuming comfort with the course content in Mathematics (8011190) and Statistics (8010848).

Outline

Intro: The Structure of Economic Data

Single-Equation Linear Models:

• Ordinary Least Squares (OLS) Estimation

1. The OLS Method
2. Mechanics and Interpretation of OLS
3. Sampling Properties
4. Hypothesis Testing
5. Issues: Omitted Variables and Measurement error

• Instrumental Variables (IV) Estimation

1. ​The IV Method
2. Multiple Instruments and Over-identified Models
3. Asymptotics
4. Pitfalls of IV: Weak Instruments
5. Testing: Endogeneity and Over-identifying Restrictions
6. Control Function Approach to Endogeneity

• Estimation and Inference by Generalized Method of Moments

 

Systems of Linear Equations (optional):

• Estimation and Inference by OLS

• Estimation and Inference by Generalized Least Squares

• The System IV estimator

 

Linear Unobserved-Effects Panel Data Models:

• Assumptions: Covariates vs Unobserved-Effects and Idiosyncratic Errors

• Estimation and Inference:

1. Pooled OLS
2. Fixed-Effects Estimation
3. First-Differencing Estimation
4. Random-Effects Estimation
5. Comparison of estimators

 

References and material

• Wooldridge J.M. (2010), Econometric Analysis of Cross-Section and Panel Data,  2nd ed., MIT Press, Cambridge (MA).
• Wooldridge J.M. (2016), Introductory Econometrics: A Modern Approach, 6th ed., Cengage Learning.
• Peracchi F. (2001), Econometrics, Wiley, Chichester (UK).

The main references are Wooldridge (2016) and Wooldridge (2010). They will be complemented by some chapters from Peracchi (2001). Lecture slides will be posted on the course web site. Suggestions for further reading will be provided in class.

 

Updated A.Y. 2017-2018

Overview

The course is organized in two modules of equal length. The first module (Static Regression) will familiarize students with the workhorses of empirical work in economics, namely the linear regression model and the ordinary least squares (OLS) estimator, and with the problems that arise when the model assumptions are violated, in particular when the regressors cannot be regarded as exogenous, that is, uncorrelated with the regression errors. The second module (IV & GMM) will pay special attention to the method of instrumental variables as a way of solving the endogeneity problem and the specific issues that arise with the use of this method.

 

Outline

Static Regression:

• Conditional expectations and best linear predictors

• The classical linear model and the OLS estimator

• Sampling properties of OLS

• Generalized least squares (GLS) and feasible GLS

• Diagnostic procedures

• Hypothesis testing and model selection

 

IV & GMM:

• Estimation of causal effects

• The instrumental variables (IV) method

• Properties of conventional IV estimators under weak instruments

• Robust inference under weak instruments

• The generalized method of moments (GMM)

 

References and material

• Angrist J.D., and Pischke J.-S. (2010), Mostly Harmless Econometrics: An Empiricist’s Companion, Princeton University Press, Princeton.
• Angrist J., and Krueger A. (2001) “Instrumental variables and the search for identification: From supply and demand to natural experiment”, Journal of Economic Perspectives, 15: 69-85.
• Bowden R.J., and Turkington D.A. (1984) Instrumental Variables, Cambridge University Press, Cambridge (UK).
• Cameron A.C. and Trivedi P.K. (2005), Microeconometrics, Cambridge University Press, New York.
• Cook R.D., and Weisberg S. (1982), Residuals and Influence in Regression, Chapman and Hall, New York.
• Hansen B.E. (2017), Econometrics, mimeo.
• Peracchi F. (2001), Econometrics, Wiley, Chichester (UK).
• Stachurski J. (2016), A Primer in Econometric Theory. MIT Press, Cambridge, MA.
• Stock J.H., and Watson M.W. (2015) Introduction to Econometrics (updated 3rd ed.), Pearson, Hoboken (NJ).
• White H. (2001), Asymptotic Theory for Econometricians (2nd ed.), Academic Press, San Diego (CA).
• Wooldridge J.M. (2010), Econometric Analysis of Cross-Section and Panel Data,  2nd ed., MIT Press, Cambridge (MA).
• Wooldridge J.M. (2001) “Applications of generalized method of moments estimation”, Journal of Economic Perspectives, 15: 87-100.

The material, including lecture notes, will be posted on the course web site. Suggestions for further reading will be provided in class.