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COURSE NAME: ECONOMETRICS

TERM: FALL

PROFESSOR: Maite Cabeza

Daniela Iorio

PROGRAM: Macroeconomic Policy and Financial Markets

ECTS: 6

HOURS: 40

OVERVIEW: The course covers basic tools needed for understandingempirical economic research. The first part of the coursefocuses on the use of the linear regression model, includingestimation and inference under basic assumptions. A quickoverview of key statistical concepts for econometrics will alsobe included. The second part of the course deals withdepartures from these basic assumptions, with special

emphasis on instrumental variable estimation. Advanced topicssuch as panel data analysis and limited dependent variables

will also be covered. Economic applications are discussedthroughout the course.

COURSE OUTLINE Part I

1. Review of statistics

Random variables and distribution. Expectations andmoments. Estimators and estimates. Sample mean andsample variance. Sampling distributions. Finite sampleproperties of estimators. Asymptotic distribution theory.

Convergence in probability. Law of Large numbers.Convergence in distribution. Central Limit Theorem.Asymptotic properties of estimators. Classical statisticalinference. Key distributions for statistical inference.

2. An introduction to linear regression

Econometrics: definition and objectives. Descriptiveeconometrics versus causalEconometrics. Experimental data and observational data.The nature of economic data. Regressions and causalinference. Conditional mean function. Linear regressionmodel with stochastic regressors. The classicalassumptions.

3. Estimation of the linear regression model

Least squares estimator (LS). Goodness of fit. Numericalproperties of LS estimator. Influential observations andoutliers. Statistical properties of LS estimator. Distribution ofLS estimator. Finite sample properties. Gauss-MarkowTheorem. Experimental approximation to LS samplingdistribution. Applications.

4. Asymptotic Theory and the linear regression model

Key concepts and properties of asymptotic theory for

econometrics. Slutskys Theorem. Continuous MappingTheorem. Transformation Theorem. Product Limit Normal

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Rule. Multivariate extension of the Central Limit Theorem.Asymptotic properties of LS. Consistent and asymptoticallynormal estimator. Applications.

5. Restrictions and hypothesis testingInference in the context of the classical linear regressionmodel. Exact tests. Hypothesis testing with the tstatistic.Hypothesis testing with the F statistic. Testing linear

combinations of regression parameters. Restricted LSestimation. F statistic via restricted and unrestricted LS.Testing for parameter stability. Confidence intervals andconfidence regions. Large sample tests. Asymptotic-T.Wald-type tests. Applications.

Part II

6. Additional Topics on OLS and Instrumental Variable (IV)Estimation

Heteroskedasticity and Robust Inference. Motivation forusing an IV approach. Multiple instruments and Two-StageLeast Squares (2SLS). Asymptotic Normality. AsymptoticEfficiency. Applications.

7. Lineal Unobserved Effects Panel Data Models

Motivation. Pooled OLS. Fixed Effects Methods. RandomEffects Methods. First Differencing Methods. Comparison ofthe estimators.

8. Limited-Dependent and Qualitative Variables

Linear Probability Model. Logit and Probit Models (MaximumLikelihood Estimation; Marginal Effects and Testing).Multinomial Logit. Ordered Response Models. Applications

9. Discrete Response Models for Panel Data and ClusterSamples

Pooled Probit and Logit. Unobserved Effects Probit andLogit models.Applications.

10. Dynamic Unobserved Effects Panel Data Models

Applications to both linear and non-linear models.

EVALUATION SYSTEM 2 exams (80%), 4 assignments (20%)

REFERENCES Davison, R.&MacKinnon, J.(2004), Econometric Theory and MetOxford Univ.Press.Green, W. (2008), Econometric Analysis. Prentice Hall. Sixth editiHayashi, F. (2000), Econometrics. Princeton University Press.

Kennedy, P. (2003), A Guide to Econometrics. Blackwell. Third e

J.M. Wooldridge, Econometric Analysis of Cross Section andPanel Data, MIT Press, Cambridge.

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