# 75367 - Econometrics

### Course Unit Page

• Teacher Iliyan Georgiev

• Credits 6

• SSD SECS-P/05

• Language English

• Teaching Material

• Course Timetable from Nov 04, 2019 to Dec 04, 2019

## Learning outcomes

At the end of the course the student is introduced to the basic concepts of econometrics, with particular focus on time series analysis. The student masters the basic least squares and maximum likelihood techniques. As for time series analysis, the student is able to apply standard ARIMA methods, with introduction to fractional integration. The student learns to apply these models using Mathlab.

## Course contents

1. Moment-conditions based estimation. Least squares and quasi maximum likelihood

2. Large-sample OLS-based inference in linear models with stochastic regressors

3. Large-sample quasi maximum likelihood inference

4. Univariate time series models for conditional means and conditional variances. Estimation and inference

Important remark. For the successful completion of the course, students are highly recommended to have followed an elementary introduction to econometrics, at the level of Chapters 4-7 of Stock and Watson's Introduction to Econometrics (3d edition). These chapters are suitable for self study by students with no preliminary exposure to econometrics. Students are also expected to be familiar with matrix algebra. At the beginning of the course, the students' entry level will be evaluated by a small test on matrix algebra and elementary econometrics.

Tsay R. (2002). Analysis of Financial Time Series. Wiley

## Teaching methods

Traditional lectures, empirical examples in a computer lab and individual exercises

## Assessment methods

The final grade is min{E+B+F, 31}, where

- E is a final exam grade in [0,30].

- B is a Moodle forum participation bonus in [0,2].

- F in [0,2] is a bonus for participation in a forecasting contest.

The final exam will be a written one and will have the duration of 90 to 120 minutes (to be decided). It will consists of two parts: theoretical exercises and questions based on estimation output. During the exam students may consult a two-sided self-written A4 sheet with whatever contents they find appropriate; this sheet should be handed in together with the answers to the exam questions.

Several discussion forums will be opened in Moodle during the lecture period. Participation in forum discussions is optional.

Additionally, students will be offered to participate in a forecasting contest where they will be asked (i) to produce a forecast of two financial quantities based on an econometric model of their choice, and (ii), to write a short report motivating their choice of a model. At most 10 students will be awarded a bonus based on the accuracy of their forecasts and the quality of the written report. Participation in the forecasting contest is optional.

A final Econometrics grade of at least 15 and an average with Statistics of at least 17.5 are necessary for the successful completion of the integrated course of Statistics and Econometrics. Students are entitled to renounce an Econometrics grade greater or equal to 15 one time only.

## Teaching tools

Econometric software: Gretl (not Matlab, as stated in the learning outcomes section)