Course 2009-2010 a.y.

8221 - TIME SERIES ANALYSIS OF ECONOMIC-FINANCIAL DATA

MM-LS - AFC-LS - CLAPI-LS - CLEFIN-LS - CLELI-LS - DES-LS - CLG-LS - M-LS - IM-LS - ACME-LS - EMIT-LS

Department of Decision Sciences

Course taught in English

Go to class group/s: 31

MM-LS (6 credits - II sem. - AI) - AFC-LS (6 credits - II sem. - AI) - CLAPI-LS (6 credits - II sem. - AI) - CLEFIN-LS (6 credits - II sem. - AI) - CLELI-LS (6 credits - II sem. - AI) - DES-LS (6 credits - II sem. - AI) - CLG-LS (6 credits - II sem. - AI) - M-LS (6 credits - II sem. - AI) - IM-LS (6 credits - II sem. - AI) - ACME-LS (6 credits - II sem. - AI) - EMIT-LS (6 credits - II sem. - AI)

Course Director:
SONIA PETRONE

Classes: 31

Instructors:
Class 31: SONIA PETRONE

Course syllabus (class group/s 31)

Course Objectives

The analysis of dynamic phenomena is extremely important in economic and financial studies. The aim of the course is to provide knowledge of the classical statistical procedures for time series analysis, but also of more modern techniques, based on dynamic linear models (or state-space models). The course intends to provide a solid methodological background and data-analysis skill, with lectures in the computer room and individual and team work. The software will be R, freely available at http://www.r-project.org//. A new user-friendly R-package, dlm, has been developed for this course, for classical and Bayesian analysis of time series by dynamic linear models.

Course Content Summary

Part I. Classical analysis of univariate time series

Descriptive techniques. Decomposition of a time series; trends, seasonality, cycle. Moving average models. Nonparametric techniques
Exponential smoothing. Forecast and model comparison
Stochastic models. Stationary processes. ARMA and ARIMA models

Parte II. Dynamic linear models for time series analysis.

Dynamic linear models for time series analysis. Examples: non-stationary series; series with structural breaks; series with stochastic volatility; multivariate time series
Estimation, forecasting and control. Kalman filter
Examples and applications to economic and financial time series Dynamic linear models for trend, seasonality, cycle. Dynamic regression by dlm
Maximum likelihood estimation of unknown parameters
Bayesian inference. Conjugate analysis. Unknown covariance matrices: simple models (discount factors)
Applications (multivariate ARMA models; dynamic regression (estimation of the term structure of interest rates), models for macroeconomic variables)
Bayesian inference and forecasting via MCMC. Recent developments

Textbooks

C. CHATFIELD, The Analysis of Time Series, Chapman & Hall/ CRC, 2004, 6^th ed.
G. PETRIS, S. PETRONE, P. CAMPAGNOLI. Dynamic Linear Models with R, Springer, New York (forthcoming).

Teaching material, lecture notes, data sets, examples, R code etc will be available on the learning space of the course.

R is freely available at http://www.cran.r-project.org/

Detailed Description of Assessment Methods

There are no mid-term exams. Instead, there are take-home assignments (about every two weeks), and a final individual or team work on the analysis of real data (about 40% of the final grade). Finally. there is a written and oral individual exam (about 60%).

Prerequisites
Suggested but not compulsory: notions of Bayesian inference.