.... "I will be teaching this course during first semester (March-July, 2018) at UCN"

## Econometrics

**Course Description**

The course deals with econometrics methods and applications designed for the analysis of cross-section and panel data models and it aims to provide the student with a solid knowledge of the most commonly used econometric estimation techniques beyond the classic OLS. At the end of this course, students will be able to apply modern econometric methods to economic problems and to follow theoretical and applied econometric literature.

This course also aims to equip students with skills to carry out independent studies and some computer programming knowledge. Regarding to this last point, I believe that writing econometric codes is very helpful to understand econometrics. Moreover, it is very rare for modern econometric software to do all the things you would like to do. Therefore, it will be necessary to write you own computer code to complete assignments that are given.

**Statistical Software**

In this course I will mainly use Stata and R for examples and homework. I will mostly provide some knowledge in programming using R. However, you are free to choose any program for problem sets. Solutions of problems sets that include some programing will be posted using R. An additional introduction class to those programs will be held for a former student.

R can be downloaded using this link. Another important program that might be useful is Rstudio. This is an integrated development environment (IDE) for R. It includes a console, syntax-highlighting editor that supports direct code execution, as well as tools for plotting, history, debugging and workspace management. Rstudio can be downloaded here.

**Prerequisites**

I assume previous exposure to linear regression and to matrix algebra. So, it would be very helpful the refresh your probability and statistic, and your linear algebra (matrix operations) before starting class. A good start is reading appendices of Greene's book or attend to MathCamp. You do NOT need previous programming knowledge.

**Evaluations**

Assignments (30%), Midterm (30%), Final exam (40%). Problem sets will be given approximately weekly intervals. I will not accept late homework assignments. Correct answer to problem sets and reading assignments will be posted on the webpage. I will drop the lowest homework score when calculating your overall grade in the course. You are allowed to work in groups on the homework, but you must write up your own solutions in your own words.

**Required Texts**

I will mainly use my notes. However, these notes are highly based on the following books:

- (Hayashi)-Hayashi, F. (2000). Econometrics. Princeton University Press.
- (PV)-Cameron, Colin A. and Pravin K. Trivedi (2005), Microeconometrics: Methods and Applications, New York: Cambridge University Press.
- (W)-Wooldridge, Jeffrey (2010), Econometric Analysis of Cross Sections and Panel Data, Cambridge: MIT press.
- (G)-Greene, W. H (2003), Econometric Analysis, 6th Edition, Person Education.
- (Ruud)-Ruud, P.A. (2000), An Introduction to Classical Econometric Theory, Oxford University Press.
- (MM)-Angrist, J. D., \& Pischke, J. S. (2014). Mastering'metrics: The path from cause to effect. Princeton University Press.
- (MHE)- Angrist, J. D., \& Pischke, J. S. (2008). Mostly harmless econometrics: An empiricist's companion. Princeton university press.

**Reading**

The reading for each topic (for 2018) can be dowloaded here.