INTRODUCTION TO ECONOMETRICS
Updated A.Y. 2018-2019
The course covers the following topics:
Part I: Introduction and review
Types of data
Review of the simple linear regression model
Part II: Multiple linear regression model
Inference with OLS estimator (hypothesis testing and confidence intervals)
Properties of OLS estimators
Part III: Further topics
Nonlinearities in the regressors
Models for binary dependent variables
Instrumental variable estimation
Models for panel data
The course offers three weekly classes. Each weekly class is composed by two 45-minutes sessions, with no break between the two sessions. The classes are aimed at introducing the foundations of the econometric theory, by illustrating the main used models, their interpretations and limits and by showing their implementation via examples and exercises.
The main textbook is “Introductory Econometrics” (5th Edition or more), by J. Wooldridge.
Another suggested reference is “Introduction to Econometrics” by J.H. Stock and M.W. Watson.
Additional material will be also provided on this website.
The final evaluation is based on a closed-book written exam, covering the entire course’s program.
It includes multiple choice questions, open questions and exercises.
Exercises on multiple linear models or further topics might consist in the interpretation and inference on one or more parameter estimates; moreover, students are expected to be able to perform an analysis of the validity of one or more models.
• Students must book for the exam.
• The exam can be taken only once per session.
• Final marks are published on the dedicated web page (normally within one week after the exam) and are uploaded on the Delphi system so to be individually received by email by candidates.
Course objectives (according to the 5 Dublin Descriptors)
Knowledge And Understanding
The course provides the students with the basic knowledge on the theory and use of the main econometric models.
The course starts with a review of the simple linear regression model, which is one of the arguments introduced in the course of Quantitative Methods II, and that the students are expected to be familiar with.
Then, generalizations to multivariate linear models, and to nonlinear specifications are given, with a particular attention on estimation and interpretation of the parameters.
The strenghts and limits of the ordinary least squares (OLS) estimator will be highlighted, situations when its use is not recommended will be discussed and alternative estimators will be given.
Applying Knowledge And Understanding
The students will be able to interpret the results of model estimation and to perform inference on the estimated parameters.
Using real datasets, they will be able to perform the estimation of the parameters of the models introduced in the course, with the help of an appropriate statistical software.
They’ll learn to produce the estimates of the parameters of different model specifications, to present them in tables and perform an analysis of their validity.
After this course, the students are expected to be able to perform all the steps of an econometric analysis: the choice of a research question (ex. “what is the effect of increasing the number of teachers on the performance of students?”,”Does dietary education at school prevent children obesity?”) and of the appropriate dataset; the choice of the benchmark model, with the identification of the dependent (response) variable and of the variable of interest (whose effect has to be measured); the estimation strategy, and the specification and estimation of alternative models; the comparison of the results obtained and the discussion of the reliability of the estimates.
The students will be able to use data to get indications about economic phenomena and to measure causal effects of one or more variables on a response variable.
They will be able to discuss the results via graphical analysis and through a tabular presentation of the estimates of various regression models.
They will be able to perform a short empirical exercise, including the following steps: the discussion of the research question and of its relevance, to description of the used data and of their nature, the presentation of the results (by tables and graphs) and their validation.
The course deepens student’s ability to perform and design a data analysis to study economic phenomena, thus complementing the operative ability acquired within the two modules of Quantitative Methods.
Further, students will be able to understand the meaning of a causal effect, the problems undermining its correct estimation, due to the common confusion between correlation and causation. All this will help the students to interpret more critically and independently the statistics reported (not always correctly) by different media.