2024/2025
Times Series Econometrics
Language:
English
ECTS credits:
3
Course Syllabus
Abstract
Time series analysis is one of the natural extensions of Econometrics I and other corresponding econometrics related courses. The focus of the course is adopting and extending techniques and results from the baseline econometrics courses to the case of time series related theoretical and empirical problems
Learning Objectives
- supposed to provide the students with a set of tools that are useful for both theoretical and empirical modeling of dynamic economic data coming in the form of both univariate and multivariate time series
- content covers (but not limited to) an overview of the crucial theoretical results of contemporary time series econometrics and of the approaches towards empirical application of these results to empirical data and tasks, including estimation of dynamic economic models and practical forecasting
Expected Learning Outcomes
- students will be able to statistically describe and analyze various dynamic economic data coming in the form of time series
- construct and analyze models of the corresponding economic processes, to construct relevant predictions of the data
Course Contents
- 1. Introduction
- 2. ARMA process
- 3. Unit root tests
- 4. Seasonality in ARIMA model
- 5. ARIMAX and SARIMAX models
Bibliography
Recommended Core Bibliography
- Makridakis, S., Wheelwright, S. C., & Hyndman, R. J. (1998). Forecasting: Methods and Applications. Cyprus, Europe: John Wiley & Sons, Inc. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.F848CE7
Recommended Additional Bibliography
- Hamilton, J. D. . (DE-588)122825950, (DE-576)271889950. (1994). Time series analysis / James D. Hamilton. Princeton, NJ: Princeton Univ. Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edswao&AN=edswao.038453134