Insegnamento a.a. 2011-2012

20236 - TIME SERIES ANALYSIS OF ECONOMIC-FINANCIAL DATA


CLMG - M - IM - MM - AFC - CLAPI - CLEFIN-FINANCE - CLELI - ACME - DES-ESS - EMIT

Department of Decision Sciences

Course taught in English

Go to class group/s: 31
CLMG (6 credits - II sem. - OP  |  SECS-S/01) - M (6 credits - II sem. - OP  |  SECS-S/01) - IM (6 credits - II sem. - OP  |  SECS-S/01) - MM (6 credits - II sem. - OP  |  SECS-S/01) - AFC (6 credits - II sem. - OP  |  SECS-S/01) - CLAPI (6 credits - II sem. - OP  |  SECS-S/01) - CLEFIN-FINANCE (6 credits - II sem. - OP  |  SECS-S/01) - CLELI (6 credits - II sem. - OP  |  SECS-S/01) - ACME (6 credits - II sem. - OP  |  SECS-S/01) - DES-ESS (6 credits - II sem. - OP  |  SECS-S/01) - EMIT (6 credits - II sem. - OP  |  SECS-S/01)
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 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 (basic notions).

Parte II. Dynamic linear models for time series analysis.

  • State space models for time series analysis. Examples: non-stationary series; series with structural breaks; series with stochastic volatility; multivariate time series.
  • Hidden Markov models. Dynamic linear models.
  • 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)
  • Analysis of multivariate time series (multivariate ARMA models; dynamic regression (estimation of the term structure of interest rates), models for macroeconomic variables)
  • Bayesian inference and forecasting via Markov chain Monte Carlo (MCMC). Recent developments. 

Detailed Description of Assessment Methods

There are no partial 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 30% of the final grade).  Finally, there is a written and oral individual exam (about 70%).


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, 2009

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/
Exam textbooks & Online Articles (check availability at the Library)

Prerequisites

Suggested but not compulsory: notions of Bayesian inference and Markov chains.
Last change 30/03/2011 12:00