The module aims to provide the main econometric tools to develop, based on the available data, an empirical analysis on the relationship between economic variables and to correctly interpret and use the results obtained. In fact, many economic decisions require quantitative answers to quantitative questions, and decisions based on empirical evidence are generally considered more helpful and effective.
The module uses a scientific language based on deductive reasoning. Technical aspects of econometrics, however, will be introduced only when necessary, whereas key attention will be given to the development of an intuitive comprehension of the material, in such a way to allow for an effective and creative use of the acquired knowledge.
At the end of the module the student is expected to (a) have critical skills with respect to empirical applications made by others and (b) be able to autonomously set up and run empirical analyses in the broad areas of economics and finance.
1. INTRODUCTION (Stock-Watson, ch.2-3)
1.1. What is econometrics?
2. REGRESSION ANALYSIS (Stock-Watson, ch.4-9)
2.1. Linear regressione with a single regressor and hypothesis testing
2.2. Linear regression with multiple regressions and hypothesis testing
2.3. Diagnostics of the regression model: specification, heteroskedasticity, autocorrelation
3. EXTENSIONS (Stock-Watson, ch.11-12)
3.1. Regression with instrumental variables
3.2. Regression with binary dependent variable
|James H. Stock, Mark W. Watson||Introduzione all'econometria (Edizione 4)||Pearson Education Italia||2016||978-8-891-90124-8|
The exam is made of one written essay and one individual homework; the final grade is given by the average of the grades in the essay and the homework, with 75% and 25% weights respectively. In order to pass the exam, it is necessary to obtain a grade not below 16/30 in the written essay.
The written essay is taken in a teaching room, lasts two hours and covers the whole program of the module. It is possible to use a calculator, but neither notes nor other teaching material. It is possible to split the essay in two parts, with each covering about half program. The grade of the essay is given by the simple average of the grades in the two parts. The first-part essay (90 minutes long) is planned in the week devoted to intermediate exams, while the second-part essay (60 minutes long) will be held together with the "primo appello" in January. Students are admitted to the second-part essay provided that their grade in the first-part exam is not lower than 16/30.
The homework is developed individually outside the teaching rooms, and can be of two types (Homework I and Homework II). Each student can choose which type of homework to deliver, but must deliver one of them. Once the deadline for delivery of Homework II has expired, it is possible to deliver Homework I only. The homework grade remains valid throughout the academic year.
The homework aims to develop critical skills with respect to empirical applications. Each student is free to choose one article from www.lavoce.info, www.voxeu.org/, www.ilsole24ore.com or other webiste, provided that it discusses an economic topic and makes use of data.
The homework consists in an essay of max. 2000 words, to be delivered to the address alessandro.bucciol[at]univr.it within the day in which the latest exam of the academic year is scheduled. The homework will pass through an antiplagiarism analysis by means of the Compilation software; it is advisable to make a personal preliminary analysis before submitting the homework.
The essay must be divided in sections in such a way to contain a) a reference to the chosen article (title, authors, link), b) a summary of the article, briefly describing its motivation, goal, methodology and results, and c) a critical comment on the methodology, also proposing alternative analyses and possible future developments. The essay must also report the word count.
The homework aims to develop analytical skills through personal data analysis in Gretl. Any student interested in this homework must write to the address alessandro.bucciol[at]univr.it communicating name, surname and ID number. He or she will then receive a number, corresponding to the dataset to be used. The text of the homework will be made available at the end of the lectures; the solution must be delivered by email within the following three days.