The aim of **Applied Statistics and Econometrics** is to provide an introduction to the practice of econometrics. While both theoretical and practical aspects are covered, emphasis will be on intuitive understanding and concepts will be illustrated with real-world applications.

Throughout, we will focus on both understanding and doing. The understanding will come from lectures, class discussions, and problem solving. The doing will come from extensive statistical software use. This course requires a quarter-long commitment. Econometrics is best learned by doing, and I will require you to do a fair amount of hands-on work.

For further details about the structure of the course, please download the syllabus. The syllabus has information about this course. In particular, it has information on how, where and when to contact me and the teaching assistants; an outline of the course content, the schedule of exam dates; **the grading policy**, and other important organizational details of the course. You can find some of this information on this web page, but not all. So, please, refer to the syllabus for any questions you may have about the course.

n. | date | topic |
---|---|---|

01 | M 13-02-2016 | Introduction to econometrics |

02 | T 14-02-2016 | Review of statistics |

03 | M 20-02-2016 | Review of statistics I |

04 | T 21-02-2016 | Review of statistics II |

05 | M 27-02-2016 | Bivariate regression I |

06 | T 28-02-2016 | Bivariate regression II |

07 | M 06-03-2016 | Bivariate regression III |

08 | T 07-03-2016 | Endogeneity and causality |

09 | M 13-03-2016 | Multiple regression I |

10 | T 14-03-2016 | Multiple regression II |

11 | M 20-03-2016 | Multiple regression II |

12 | T 21-03-2016 | Nonlinear regression models I |

13 | M 27-03-2016 | Nonlinear regression models II |

14 | T 28-03-2016 | Midterm |

15 | M 03-04-2016 | Assessing regression studies |

16 | T 04-04-2016 | ~ |

17 | M 10-04-2016 | Binary dependent variable I |

18 | T 11-04-2016 | Panel Data I |

19 | T 24-04-2016 | Panel Data II |

20 | T 02-05-2016 | Instrumental variables regression I |

21 | M 08-05-2016 | Instrumental variables regression II |

22 | T 09-05-2016 | Applications |

There will be a weekly problem set. The assignments will involve both theoretical and empirical work. Group study and free discussion are encouraged. But you should submit your own answers. You will probably find the class very hard to follow if you fail to spend sufficient time on all of the problem sets. The problem sets are part of the final grade as explained in the next section below.

If you have any question on the problem sets, please ask me or TA’s during our office hours. Our office hours are for you. I prefer to talk to you in person. I feel that Email is not a very efficient way to ask econometric questions.

Problem set answers are to be turned in on time. You can hand in the homework AFTER the class. Please do not come and hand it in to me whilst I am lecturing. Do not email assignments. Late solution will not be accepted!

Problem Set 2 (Due Tuesday, March, 7 2017)

*Note:*This problem set is multiple choice. You have to print the answer sheet, fill your id, and fill your answers based on the questions.Problem Set 3 (Due Thursday, March 16 2017) This problem set is personalized. Each student must download her own version of the problem set. To download the problem set use the following URL:

`http://gragusa.org/ps3/xxxxxx.pdf`

where`xxxxxx`

is the LUISS ID of the student.Problem Set 4 (Due Tuesday, April 11 2017) This problem set is personalized. Each student must download her own version of the problem set. To download the problem set use the following URL:

`http://gragusa.org/ps4/xxxxxx.pdf`

where`xxxxxx`

is the LUISS ID of the student.Problem Set 5 (Due Tuesday, May 2 2017) This problem set is personalized. Each student must download her own version of the problem set. To download the problem set use the following URL:

`http://gragusa.org/ps5/xxxxxx.pdf`

where`xxxxxx`

is the LUISS ID of the student.

The TAs will lead a weekly practice session which will be held in the computer labs (301 and 306).

The TA sessions are an important part of the course and regular and
attendance is strongly encouraged. During these sessions, the TA will review the concepts introduced in class and solve applied problems using `R`

.

The sessions are also the right place to ask questions about the problem set of the week.

The following table gives the distribution of students across the two sessions:

TA | Where | Time | Students |
---|---|---|---|

Siria Angino | A301 | W 14:00-15:30 | G-Z |

Giuseppe Brandi | A306 | W 18:30-20:00 | A-F |

The software used in this course is R. No prior knowledge of this software package is assumed. This package will be introduced in the TA Sessions.

`R`

is installed on all computers in the computer lab. Since
R is open source you can freely install it on your laptop or
desktop. R is available for all major computing
platforms: Windows, Mac OSX, and Linux. Platform specific installation help can
be found at
here.

I recommend using `R`

through Rstudio, a free and open
source integrated development environment (IDE) for `R`

.
Rstudio is installed on most computers in the computer
labs where TA sessions are held. You can easily install Rstudio on your machine by following the instruction here.

**Instructor:** Giuseppe Ragusa

**Teaching assistants:**

- Siria Angino
- Giuseppe Brandi

**Office hours**

Angino: W, 16:00-17:00

Brandi: W, 9:30-11:00

Ragusa: T, 11:30-13:00

University of Pisa

Via Cosimo Ridolfi 10

56124 Pisa (PI)

Italy

- giuseppe.ragusa@gmail.com
- @giuragusa on Twitter.
- On GitHub.

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