20236 - TIME SERIES ANALYSIS OF ECONOMIC-FINANCIAL DATA
Course taught in English
Go to class group/s: 31
Class-group lessons delivered on campus
Basic notions of Statistics and Probability.
The analysis of dynamic phenomena is extremely 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.
- Aims of time series analysis and descriptive techniques:
- Time series decomposition. Exponential smoothing.
- Probabilistic models for time series analysis:
- Time series as a discrete time stochastic process.
- Stationary processes. Summaries; estimation of the autocorrelation function.
- Examples: White noise. Gaussian processes. Random walks.
- Markov chains: basic notions; inference for Markov processes.
- ARMA and ARIMA models: basic notions; inference and prediction.
- State space models for time series analysis:
- Motivating examples: non-stationary series; structural breaks; stochastic volatility; streaming data; multivariate time series.
- State space models: definition and main properties.
- Hidden Markov models. Dynamic linear models (dlm).
- Estimation, forecasting and control. Kalman filter.
- Examples for economic and financial time series. Dynamic linear models for trend, seasonality, cycle.
- Dynamic regression by dlm.
- Maximum likelihood estimation of unknown parameters.
- Bayesian inference.
- Analysis of multivariate time series (multivariate ARMA models; dynamic regression-estimation of the term structure of interest rates; seemingly unrelated time series models; factor models).
- Bayesian inference and forecasting via Markov Chain Monte Carlo (MCMC).
- Recent developments.
- Explain and describe the main statistical methods for time series analysis.
- Identify the models, estimate and make forecasts for dyamic systems, both stationary and non-stationary, with an adeguate quantification of uncertainty and risk.
- 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.
- Face-to-face lectures
- Exercises (exercises, database, software etc.)
- Group assignments
- Exercises: lectures in the computer room ('laboratories') on 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.
|Continuous assessment||Partial exams||General exam|
- There are no partial exams, but 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) .
- 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.