1. Home
  2. NOVA School of Business and Economics
  3. Master's (International) in Finance
  4. Microeconometrics

Microeconometrics

Objectives

This class will cover a range of advanced econometric techniques frequently employed in the analysis of micro data (at the household, individual level). The class combine theory and empirical applications and the lectures will include examples and discussions of empirical papers that employ the diferent techniques.

General characterization

Code

2165

Credits

7

Responsible teacher

Teresa Molina Millán

Hours

Weekly - Available soon

Total - Available soon

Teaching language

English

Prerequisites


Bibliography

Textbooks:

  • 1. Angrist, J. and Prischke, J. Mostly Harmless Econometrics: An Empiricist’s Companion. Prince- ton Univ Press, 2009.

  • 2. Cameron, C.A. and Trivedi, P.K. Microeconometrics: methods and applications. Cambridge U.P., 2005.

  • 3. Cameron, A. C., and Trivedi, P. K. Microeconometrics using stata, 2010.

  • 4. Wooldrige, J. M. Econometric Analysis of Cross Section and Panel Data. The MIT Press, 1st edition, 2002.

Teaching method

Lectures will cover the core theoretical materials and discussions of empirical papers that employ the diferent techniques. Background reading is expected.

Evaluation method

There will be four applied homework assignments that will account for 30% of your final grade. You are expected to complete all the assignments in time.

    Due dates:

  • Problem Set 1: OLS, GLS, IV and Quantile. Due March, 2nd.

  • Problem Set 2: Discrete Choice Models and Censoring. Due March, 16th.

  • Problem Set 3: Panel Data and RCT. Due April, 13th.

  • Problem Set 4: Diff-in-Diff, Matching and RDD. Due May, 10th

There will be a midterm exam (April, 2nd 9:30-11:00) and a final exam which will account for 30 and 40% of the final grade respectively. In accordance with the school norms, there is no procedure for grade improvement after passing a course (no re-sit or second course enrollment).

Subject matter

  • 1. Introduction: brief review of OLS and Hypothesis testing;

  • 2. Beyond OLS: Generalized Least Squares (GLS),Instrumental Variables and Quantile Regression;

  • 3. Discrete choice models (MLE): Linear probability model. Latent variable models: the Probit and the Logit. Multinomial response models and Censored Data;

  • 4. Panel Data Models. Between and within variation. Random and fixed effects;

  • 5. Estimating Average Treatment Effects. Experiments and Quasi-experiments. Differences in Dif- ferences, Propensity Score Matching and Regression Discontinuity Design;

  • 6. Other topics: Attrition and Sample Selection, Bootstrapping, Multiple testing, Factor Analysis and Index Variables.