Insegnamento a.a. 2019-2020

20236 - TIME SERIES ANALYSIS OF ECONOMIC-FINANCIAL DATA

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) - 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) - GIO (6 credits - II sem. - OP  |  SECS-S/01) - DSBA (6 credits - II sem. - OP  |  SECS-S/01) - PPA (6 credits - II sem. - OP  |  SECS-S/01) - FIN (6 credits - II sem. - OP  |  SECS-S/01)
Course Director:
SONIA PETRONE

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


Suggested background knowledge

Basic notions of Statistics and Probability.

Mission & Content Summary

MISSION

The analysis of dynamic phenomena is crucially important in economic and financial studies. The course aims at providing solid methodological background and data-analysis skills for time series analysis, covering classical as well as modern techniques for non stationary time series, based on state-space models.

CONTENT SUMMARY

  1. Aims of time series analysis and descriptive techniques:
    • Time series decomposition. Exponential smoothing.
  2. Probabilistic models for time series analysis:
    • Time series as a discrete time stochastic process.
    • Stationary processes. Summaries. Estimation of the autocorrelation function.
    • First examples: White noise. Gaussian processes. Random walks.  
    • Categorical time series: Markov chains. Inference for Markov processes.
    • Stationary time series: ARMA models (brief review). 
    • Time series with structural breaks: Hidden Markov Models.
  3. State space models for time series analysis:
    • Motivating examples: non-stationary series; stochastic volatility; streaming data.
    • State space models: definition and main properties.
    • Hidden Markov models as state-space models.
    • Dynamic linear models (DLM).                   
    • Filtering, forecasting, smoothing: Kalman filter and Kalman smoother.  
    • Innovation process and model checking.        
    • Maximum likelihood estimation of unknown parameters. 
    • Examples for economic and financial time series. DLMs for trend, seasonality, cycle.
    • Nonlinear regression by DLMs.
    • ARMA models as DLMs.
    • Multivariate time series (dynamic regression (example: term structure of interest rates); seemingly unrelated time series models; factor models).
    • Bayesian inference and forecasting via Markov Chain Monte Carlo (MCMC).
    • Recent developments.                    


Intended Learning Outcomes (ILO)

KNOWLEDGE AND UNDERSTANDING

At the end of the course student will be able to...
  • Explain and describe the main statistical methods for time series analysis.
  • Identify the models suitable for the problems under study; estimate and make forecasts for dynamic systems, both stationary and non-stationary, with an adeguate quantification of uncertainty and risk.  
  • Use R for time series analysis.

APPLYING KNOWLEDGE AND UNDERSTANDING

At the end of the course student will be able to...
  • Apply and properly interpret the models and methods presented in the course in applications.
  • Use adeguate statistical software (R and main R functions for time series analysis). 
  • Evaluate and justify their analysis on real data.
  • Prepare appropriate reports of their statistical analysis in real data applications. 

Teaching methods

  • Face-to-face lectures
  • Exercises (exercises, database, software etc.)
  • Group assignments

DETAILS

  • Exercises: lectures in the computer room ('laboratories') on the analysis of real data. Software: R, freely available at www.r-project.org. An R-package, 'dlm', has been developed for this course.
  • Students are involved in the learning process through individual and team work in periodic assignments.


Assessment methods

  Continuous assessment Partial exams General exam
  • Written individual exam (traditional/online)
     x
  • Group assignment (report, exercise, presentation, project work etc.)
 x    

ATTENDING AND NOT ATTENDING STUDENTS

  • There are no partial exams, but there are about 4 take-home assignments (individual or team work). Assigments are not mandatory, but strongly encouraged for an active learning. They are not evaluated for the final exam; yet, students who did not deliver the assignments have to answer additional questions on data-analysis with R  in the written proof.
  • A final project on real data analysis (individual or team work) is mandatory and evaluated for the final exam (30%).
  • Written proof (70%; it can be 100% if poorly done) .

Teaching materials


ATTENDING AND NOT ATTENDING STUDENTS

  • C. CHATFIELD, The Analysis of Time Series, Chapman & Hall/CRC, 2004, 6th edition.
  • G. PETRIS, S. PETRONE, P. CAMPAGNOLI, Dynamic Linear Models with R, Springer, New York, 2009.
  • S. PETRONE, Lecture notes: Introduction to Markov Chains, 2015.
  • Lecture notes, data sets, R code, R Markdown templates etc are made available on the  Bboard of the course.
Last change 15/06/2019 08:32