EC906-ECONOMETRICS: First Part

EC976-ECONOMETRICS:

First Term: Valentina Corradi

Second Term: Gianna Boero

First term instructor: Valentina Corradi

Office: Social Studies Building S1.112

Tel: 765 28414, e-mail:

Webpage:

Office Hours: Tuesday 10.15am-12.15pm or by appointment

Organization:

Two lectures per week : Monday 3-4pm L5 (Sciences) and Tuesday 1-2pm S0.21 (Social Sciences)

There will be an hour of class per week: students will be assigned to one group.

Classes start in week 2.

Weeks 2-4 classes will be given by the Teaching Assistant, Milan Nedeljkovic, who will focus on the use of computer packages and empirical applications.

Weeks 5-10 classes will be given by the instructor. Classes will focus on methodological and applied exercises and on homework solutions and discussion.

Aims and Objectives:

This module provides a solid training in econometrics with an emphasis on empirical modelling of economic data. It will enable students to develop necessary skills to carry out good quality empirical research. This module is a postgraduate-level introductory econometrics. Prerequisites: a thorough knowledge of undergraduate-level statistics and mathematics for economics/business.

By the end of the course the student would have developed (i) a deeper and broader knowledge and understanding of material needed for empirical quantitative analysis; (ii) the habit of thought, knowledge and understanding in order to carry out good quality applied econometric research; (iii) the necessary skills to interpret and communicate their results to a non-technical audience; (iv) the critical insight to appraise econometric results obtained by other researchers. The emphasis throughout the module is on the application of standard techniques.

Assessment:

There will be a 1-hour exam in the first week of the second semester. The exam will be based on the material taught in the first semester. It will count for 10% of your final mark. By the end of term 2 you need to complete a empirical projects, which will count for 15% of your mark. The remaining 75% of your mark is based on your final examination in May-June.

Part 1:

Required Textbook: Wooldridge, J.M, Introductory Econometrics: A Modern Approach, South Western, Second Edition.

Lecture Notes will also be distributed.

Other books:

Greene, W.: Econometric Analysis, Prentice Hall, 5th Edition, 2003 (harder than Wooldridge)

Stock J.H. and M.W. Watson, Introduction to Econometrics, Addison Wesley, 2003 (softer than Wooldridge)

Syllabus

(1) Economic Data and Econometric Analysis. (Ch.1 Wooldridge)

(2) The Basic Linear Model. (i) deriving Ordinary Least Squares (OLS) estimators, (ii) algebraic properties of OLS, (iii) expected values, variance and estimated standard error of OLS. (Ch.2 Wooldridge)

(3) The Multiple Linear Regression Model: Estimation. (i) derivation of OLS estimators via matrix algebra, (ii) interpretation and mechanics of OLS estimators, (iii) Goodness of fit (iv) Expected values of OLS estimators: omitted variables, irrelevant variables, (v) Variance of OLS estimators, (vi) Efficiency: Gauss-Markov Theorem. (Ch.3 Wooldridge)

(4) The Multiple Linear Regression Model: Inference. (i) Sampling Distribution of OLS estimators, (ii) Confidence Intervals (iii) Hypothesis Testing about one linear combination of parameters, (iv) Testing Multiple Restrictions. (Ch. 4 Wooldridge)

(5) Large Sample Properties of OLS estimators. (i) Consistency, (ii) Asymptotic Normality, (iii) Asymptotic Efficiency. (Ch.5 Wooldridge)

(6) Heteroskedasticity. (i) Consequences of heteroskedasticity on OLS estimator and OLS based inference, (ii) Heteroskedastic robust inference, (iii) Testing for Heteroskedasticity: White’s test, (iv) Weighted Least Square. (Ch.8 Wooldridge)

(7) Instrumental Variables Estimation and Two-Stage Least Squares. (i) Motivation: omitted variables, endogeneity, measurement errors (ii) Instrumental Variable Estimation of Linear Regression Model (iii) Two- Stage Least Squares 2SLS, (iv) Testing for Endogeneity and for Overidentifying Restrictions (Wooldridge Ch. 15)

(8) Limited Dependent Variable Models. (i) Linear Probability Model, (ii) Logit Model, (iii) Probit Model. (Wooldridge Ch.7.5-7.6 and Ch. 17.1-17.2).