The course provides an introduction to the use of statistical and econometric methods in the social sciences, with particular but not exclusive reference to applications in economics. Among the themes treated, of particular relevance are the problems of estimation and test of hypotheses within the multivariate linear regression model, instrumental variable estimation, discrete choice models, and the analysis of panel data. The course is applied in nature, and use will be made of the software Stata, during a series of ten "Stata labs". Students also will have to write a "Stata project" which will account for half of the final grade.

Students taking this class should have taken an introductory course in statistics, and in particular they should be familiar with the following concepts: random variables, main distribution of probabilities, estimation of the mean of a population and test of hypothesis. Students who do not satisfy these prerequisites may take the course anyway, but they should be prepared to work hard to remedy their ignorance.

At the end of the course, successful students will be able to autonomously employ the methods considered in the course, while appropriately interpreting the results of their analyses.

Stata Lab

The exercises using Stata will take place on Thursdays between 3pm and 5pm at the Labic Computer Lab. There the students will also find a number of personal computers, with Stata installed, for autonomous use during the lab's opening hours.

References

The main textbook is: Stock, James and Mark W. Watson. 2012. Introduction to Econometrics. 3rd edition. Pearson (indicated in what follows as [SW])
Also relevant is: Ray C. Fair. 2012. Predicting Presidential Elections and Other Things. 2nd. edition. Stanford University Press (The PDF file of the first edition is: avialable online). A copy is available at the Central library.

To review the topics whose knowledge is a prerequisite for this course, students may peruse any introductory text in statistics. Students who can read Italian may want to consider: Pacini, B. and Picci, L. 2001. Introduzione alla Statistica, Clueb (indicated in what follows as [PP]). A limited number of copies of this textbook may be borrowed from the library.

Syllabus

Introduction (Week 1 & 2)
A bird's aye view of the main topics of the course
On the history of econometric thought, see (in Italian) Le veritÃ sfuggenti dell'econometria, Lucio Picci, 2000.
Quick review of the following themes: descriptive statistics; probability, random variables and probability distributions, sample analysis, estimation, test of hypotheses [SW] chapter 2, 3. [PP]: Chapter 1: All ; Chapter 2: All, with the exlusion of 2.10; Chapter 3: 3.1, 3.2, 3.4; Chapter 4: All, with the exclusion of 4.9 (Bayes' Theorem); Chapter 5. All, except 5.6 (Binomial distribution), 5.7 (Ipergeometric distribution) ; Chapter 6. All, except 6.6 (Sample distribution of a relative frequency); Chapter 7. All, except 7.6 (Interval estimation of a relative frequency); Chapter 8. All, except 8.4 (Test of hypothesis for a relative frequency) and 8.6 (Other cases)
Stata Lab 1: Introduction to the Stata. Basic commands, data management, use of ".do files".
Stata Lab 2: Exploratory data analysis.

The linear regression model with a single regressor (Week 3)
Esimation of the coefficients and test of hypothesis on the regression coefficient. [SW] Chapter 4 & 5, all sections.
Stata Lab 3: The linear regression model with a single regressor.

The linear multivariate regression model (Week 4)
The model and estimation of the regression coefficients. [SW] Chapter 6 and 9 (see also: 17, 18.1, 18.2, 18.4 and 18.5).
Stata Lab 4: The linear multivariate regression model, Part I.

Test of hypothesis in the linear multivariate regression model (Week 5)
T-test and F-test. [SW] Chapter 7 (see also: 18.3)
Stata Lab 5. The linear multivariate regression model, Part II.

The analysis of the linear regression model and heteroskedasticity (Week 6)
Heteroskedasticity, testing, and robust standard error estimation. [SW] Chapter 7 all sections (see also: 18.6).
Analysis of the regression model. [SW] Chapter 9.
Stata Lab 6. Heteroskedasticity and GLS estimation.

Instrumental Variable Estimation (Week 7).
Instrumental variable and 2SLS estimation. [SW] Chapter 12 (all sections) (also see: 18.7)
Stata Lab 7. IV estimation

Panel data models (Week 8)
Panel data structure.
"Pooled" and "Fixed Effects" models. [SW] Chapter 10 (all sections).
Stata Lab 8. Panel data models.

Discrete choice models (Week 9)
[SW] Chapter 11 (all sections).
Stata Lab 9. Estimation and analysis of Probit and Logit models.

Time series data and models. Review of topics (Week 10)
Time series models. [SW] Chapter 14 (Sections 1-5; also, read Section 6).
Stata Lab 10. Wrap-up of the course.

Evaluation

The final grade will be based on one midterm exam (25%), one final exam (25%), and one Stata project (read the instructions here), which will be split into two parts (25% each). The first midterm exam will be taken after Week 5 of the course.

## Applied Econometrics, Academic Year 2013-2014

## Instructor: Lucio Picci

## Department of Economics, University of Bologna

## Aims, objectives and learning outcomes

The course provides an introduction to the use of statistical and econometric methods in the social sciences, with particular but not exclusive reference to applications in economics. Among the themes treated, of particular relevance are the problems of estimation and test of hypotheses within the multivariate linear regression model, instrumental variable estimation, discrete choice models, and the analysis of panel data. The course is applied in nature, and use will be made of the software Stata, during a series of ten "Stata labs". Students also will have to write a "Stata project" which will account for half of the final grade.Students taking this class should have taken an introductory course in statistics, and in particular they should be familiar with the following concepts: random variables, main distribution of probabilities, estimation of the mean of a population and test of hypothesis. Students who do not satisfy these prerequisites may take the course anyway, but they should be prepared to work hard to remedy their ignorance.

At the end of the course, successful students will be able to autonomously employ the methods considered in the course, while appropriately interpreting the results of their analyses.

## Stata Lab

The exercises using Stata will take place on Thursdays between 3pm and 5pm at the Labic Computer Lab. There the students will also find a number of personal computers, with Stata installed, for autonomous use during the lab's opening hours.## References

The main textbook is: Stock, James and Mark W. Watson. 2012. Introduction to Econometrics. 3rd edition. Pearson (indicated in what follows as [SW])Also relevant is: Ray C. Fair. 2012. Predicting Presidential Elections and Other Things. 2nd. edition. Stanford University Press (The PDF file of the first edition is: avialable online). A copy is available at the Central library.

To review the topics whose knowledge is a prerequisite for this course, students may peruse any introductory text in statistics. Students who can read Italian may want to consider: Pacini, B. and Picci, L. 2001. Introduzione alla Statistica, Clueb (indicated in what follows as [PP]). A limited number of copies of this textbook may be borrowed from the library.

## Syllabus

A bird's aye view of the main topics of the course

On the history of econometric thought, see (in Italian) Le veritÃ sfuggenti dell'econometria, Lucio Picci, 2000.

Quick review of the following themes: descriptive statistics; probability, random variables and probability distributions, sample analysis, estimation, test of hypotheses [SW] chapter 2, 3. [PP]: Chapter 1: All ; Chapter 2: All, with the exlusion of 2.10; Chapter 3: 3.1, 3.2, 3.4; Chapter 4: All, with the exclusion of 4.9 (Bayes' Theorem); Chapter 5. All, except 5.6 (Binomial distribution), 5.7 (Ipergeometric distribution) ; Chapter 6. All, except 6.6 (Sample distribution of a relative frequency); Chapter 7. All, except 7.6 (Interval estimation of a relative frequency); Chapter 8. All, except 8.4 (Test of hypothesis for a relative frequency) and 8.6 (Other cases)

Stata Lab 1: Introduction to the Stata. Basic commands, data management, use of ".do files".

Stata Lab 2: Exploratory data analysis.

Esimation of the coefficients and test of hypothesis on the regression coefficient. [SW] Chapter 4 & 5, all sections.

Stata Lab 3: The linear regression model with a single regressor.

The model and estimation of the regression coefficients. [SW] Chapter 6 and 9 (see also: 17, 18.1, 18.2, 18.4 and 18.5).

Stata Lab 4: The linear multivariate regression model, Part I.

T-test and F-test. [SW] Chapter 7 (see also: 18.3)

Stata Lab 5. The linear multivariate regression model, Part II.

Heteroskedasticity, testing, and robust standard error estimation. [SW] Chapter 7 all sections (see also: 18.6).

Analysis of the regression model. [SW] Chapter 9.

Stata Lab 6. Heteroskedasticity and GLS estimation.

Instrumental variable and 2SLS estimation. [SW] Chapter 12 (all sections) (also see: 18.7)

Stata Lab 7. IV estimation

Panel data structure.

"Pooled" and "Fixed Effects" models. [SW] Chapter 10 (all sections).

Stata Lab 8. Panel data models.

[SW] Chapter 11 (all sections).

Stata Lab 9. Estimation and analysis of Probit and Logit models.

Time series models. [SW] Chapter 14 (Sections 1-5; also, read Section 6).

Stata Lab 10. Wrap-up of the course.

## Evaluation

The final grade will be based on one midterm exam (25%), one final exam (25%), and one Stata project (read the instructions here), which will be split into two parts (25% each). The first midterm exam will be taken after Week 5 of the course.## Office Hours

Lucio Picci's Office Hours (check the bottom of the page)## Mailing list of the course

Students are required to subscribe to this Googlegroups mailing list. Please contact the instructor if you experience problems in subscribing.Lucio Picci, 29 August 2013