INFORMAZIONI SU

Time Series analysis

CORSO DI STUDIO: Corso di Laurea Magistrale in Economics-Scienze Economiche       a.a. 2015/2016

Denominazione insegnamento/Course Title

Time Series Analysis

Lingua dell’insegnamento: Inglese
Crediti e ore di lezione: 6 CFU, 48 ore di lezione

Moduli: NO

Settore/i scientifico disciplinare: SECS-S/03

Docente: Luca Grassetti
Indirizzo email: luca.grassetti@uniud.it
Pagina web personale: http://people.uniud.it/page/luca.grassetti

Prerequisiti e propedeuticità/Requirements

The course takes place in the second semester of the second year of Economics master degree course.
The course is the last of a set of statistics and econometrics courses in the Economics Master Degree. Notwithstanding, none bridging course is provided for the present teaching activity.
Preliminary knowledge consists of basic statistics and econometrics topics (linear models and time series ARMA models). Advanced statistics courses (as for instance Statistics 2 course) can help the students' learning process.

Conoscenze e abilità da acquisire/Knowledge and skills

The course unit aims to raise the knowledge on econometric analysis of macro-economic time series and panel data.

Course specific knowledge

The course supply the students with specific tools for the quantitative analysis of univariate and multivariate time series and panel data. The basic models are given introducing the advanced methods as additional arguments. At the end of the course unit the students will be able to:
• recognise the main kind of macro-economic data;
• decide how to treat the specific kind of data;
• solve the classical issues involved in the time series and panel data analyses;
• interpret the model estimation results;
• develop an empirical analysis on longitudinal economic data;
• predict the behaviour of economic variables;
• apply R for the time series and panel data analyses;
• appreciate the potentials of LaTeX text editing software.

Soft skills

• topics faced during the semester introduce the statistical tools that students can use during the development of the master degree thesis;
• the students will be able to apply the optimal statistical tool given the empirical framework;
• the empirical and theoretical homeworks aim at developing the communication skills of students using the ability to synthetise based on statistical summary statistics.

Programma e contenuti dell'insegnamento/Course description

After a brief preface we will focus on three main arguments:
1. Univariate time series analysis:
• ARIMA models
• Seasonal ARIMA models
2. Multivariate time series analysis
• VAR models
3. Panel data analysis
• Fixed effects models
• Random effects models
LaTeX text edit software will be introduced during the teaching period.

Modalità di verifica dell'apprendimento/Examination

The final examination consists in:
• a compulsory final written exam (with 4 theoretical questions and an exercise). The exam is 1 hours and half long;
• a short essay on an additional topic that must be presented at the end of the course (compulsory);
• an empirical analysis developed on a realistic dataset (compulsory);
• an optional oral examination.
The written exam and the two homeworks contribute to the final mark for 20, 5 and 5 points respectively. In order to be considered in the final mark computation every individual work must be positively evaluated. The written exam aims at testing the theoretical skills while the homeworks are used to evaluate the capacity study autonomously and to apply the studied concepts. The optional oral exam is an integration of the written exam. A maximum of three points can be assigned to this test. The test can also be negatively evaluated. Honours will be assigned to students of marked excellence.

Testi / Bibliografia/Bibliography

Chapter 3 in R.H. Shumway and D.S. Stoffer. “Time Series Analysis and Its Applications (With R Examples)” (liberamente scaricabile dalla rete)

Chapters 1 e 2 in H. Lütkepohl. “New introduction to multiple time series analysis”. Springer Science & Business Media, 2005.

Chapters 1, 2 e 3 in C. Hsiao. “Analysis of panel data”. Vol. 54. Cambridge university press, 2014.

W.H. Greene. “Econometric Analysis – Seventh Edition”, Pearson, New York, 2012.

Strumenti a supporto della didattica/ Further readings and support material

Course slides (that will be released during the semester) cover the entire course programme but they will be integrated with some other didactic materials.
The main topics will follow the textbooks outline. Some specific arguments will be developed following specific alternative material.
Around 50% of the course will be based on exercise lectures developed in the laboratory using R to develop some analysis both on didactic and real data.
During the course students will be asked to develop two homeworks:
- a short essay on an additional topic that must be presented at the end of the course;
- an empirical analysis developed on a realistic dataset.

Course slides (theory and exercise) available on “Materiale didattico” website. Additional didactic materials (free available on the web or on “Materiale didattico” website):
• note “Econometrics In R” di Farnsworth;
• “Panel Data Econometrics in R: The plm Package” by Y. Croissant and G. Millo;
• “Vector Autoregressive Models” by H. Luetkepohl EUI Working Papers
• “VAR, SVAR and SVEC Models: Implementation Within R Package vars” by B. Pfaff;

Tesi di laurea/Thesis

It is possible to develop the master degree thesis within the course framework. The thesis work can be both empirical and theoretical.

Note/Remarks

The present course aims at introducing the students to the empirical analysis of economic datasets. Consequently the specific teaching activity can be considered as propaedeutic to the development of specific empirical analyses proposed in the other Master degree course units.
The students’ evaluation does not present differences between attenders and non-attenders. Non-attenders must participate to homeworks too.