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Time Series Analysis

Objectives

1. To understand volatility models for conditionally heteroscedastic time series
2. To understand multivariate models
3. To understand the analysis of repeated measures design
4. To understand how periodograms are used with time series data
5. To understand how spectral density estimation and spectral analysis is used for
6. To understand fractional differencing and threshold models

General characterization

Code

200191

Credits

4.0

Responsible teacher

Docente a designar

Hours

Weekly - Available soon

Total - Available soon

Teaching language

Portuguese. If there are Erasmus students, classes will be taught in English

Prerequisites

   

Bibliography

- Shumway, R.H. and Stoffer, D.S. (2011). Time Series Analysis and its Applications with Examples in R, 3rd edition, Springer.
- Hyndman, R. J., Athanasopoulos, G. (2018). FORECASTING: PRINCIPLES AND PRACTICE, 2nd edition
- Tsay, R. (2013). An introduction to Financial Data with R, Wiley.

Teaching method

The curricular unit is based on theoretical and practical lessons. A variety of instructional strategies will be applied, including lectures, slide show demonstrations, step-by-step applications (with and without software), questions and answers. The sessions include presentation of concepts and methodologies, solving examples, discussion and interpretation of results. The practical component is geared towards solving problems and exercises, including discussion and interpretation of results. A set of exercises to be completed independently in extra-classroom context is also proposed.

 

Evaluation method

Evaluation:
1st call: project (40%), first round exam (60%)
2nd call: final exam (100%)

Subject matter

1. Volatility models

2. VARMA models

3. Repeated measure analysis

4. The periodogram

5. Spectral analysis

6. Fractional differencing and threshold models