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Course 2007-2008 a.y.

8221 - TIME SERIES ANALYSIS OF ECONOMIC-FINANCIAL DATA


MM-LS - OSI-LS - AFC-LS - CLAPI-LS - CLEFIN-LS - CLELI-LS - CLEACC-LS - DES-LS - CLEMIT-LS - CLG-LS - M-LS
Department of Decision Sciences

Course taught in English


Go to class group/s: 31

MM-LS (6 credits - II sem. - AI) - OSI-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) - CLEACC-LS (6 credits - II sem. - AI) - DES-LS (6 credits - II sem. - AI) - CLEMIT-LS (6 credits - II sem. - AI) - CLG-LS (6 credits - II sem. - AI) - M-LS (6 credits - II sem. - AI)
Course Director:
SONIA PETRONE

Classes: 31 (II sem.)
Instructors:
Class 31: SONIA PETRONE



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 is SPSS and/or R for the first part of the course, and R for the second part of the course. Background highly recommended: basic notions of statistical inference and regression.


Course Content Summary

Part I.  Classical analysis of univariate time series

  • Descriptive techniques. Decomposition of a time series; trends, seasonality.
  • Stochastic models. Stationary processes. Models in the time domain; ARMA and ARIMA models.
  • Exponential smoothing. Forecast and model comparison.

Parte II. Dynamic linear models for time series analysis.

  • Dynamic linear models for univariate and multivariate time series. Estimation and forecasting. Kalman filter. Bayesian approach.
  • Maximum likelihood estimation of unknown parameters. Unknown covariance matrices.
  • Bayesian inference. Conjugate analysis. Markov chain Monte Carlo techniques.
  • Model specification. Structural DLM. Trend and seasonal components. Regression components.
  • Advanced topics (Models for series with structural breaks. Stochastic volatility models. Financial applications).

Detailed Description of Assessment Methods

The exam consists of individual and team work on the analysis of real data and of an individual final exam.


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

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

Exam textbooks & Online Articles (check availability at the Library)
Last change 13/06/2007 11:18
 

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