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Regular version of the site

Time Series

2019/2020
ENG
Instruction in English
3
ECTS credits
Course type:
Elective course
When:
3 year, 1 module

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
• construct relevant predictions of the data

Course Contents

• Inroduction to the course
The difference between time series and random samples. The difference between time series econometrics and cross-sectional econometrics. Opportunities and goals of time series econometrics. Examples of time series in economic life.
• Stationarity
Strong and weak stationarity. Examples of non-stationary time series. Typical types of non-stationarity in economic time series. Trends, types of trends. Structural shifts. Random walk. Non-stationarity in the variance. Formal tests for stationarity. Extended Dickey-Fuller criterion. The Kwiatkowski-Philips-Schmidt-Shin test. Stationary transformations. Difference transformations. Growth and growth rates. Log-differences. Interpretation of standard increments. The relationship between log differences and growth rates.
• Linear regression for stationary and ergodic time series
Ergodicity of the time series. The ergodic theorem. Centarl limit theolrema for dependent observations. Long-term desperation. Newey-West score. The method of least squares in linear regression for stationary time series. Model hypotheses and properties of OLS estimates. Popraka on autocoerrelation of residues. The problem of endogeneity. The distributed lag model and its variants. Testing complex hypotheses. Estimation of the commulative effect of a distributed lag model.
• Forecasting a single time series.
Conditional mathematical expectation and the best in the mean-square sense of the forecast. Forecasting from linear models. Static and dynamic forecasts. Reliability of the forecast. Predictive quality metrics. Sarvnenie predictive strength of models. Validation and forecasting schemes. Alternative loss functions.
• Stationary linear regression
Autoregression processes. Moving average processes. Autoregression-moving average processes. Stationarity of processes. Random walk. Single roots. Characteristics of stationary linear processes. Wold's theorem. Box-Jenkins modeling and forecasting methodology.
• Structural breaks and stability

• Quiz
• Home work
• Exam

Interim Assessment

• Interim assessment (1 module)
0.67 * Exam + 0.264 * Home work + 0.066 * Quiz

Recommended Core Bibliography

• Bell, W. R., Holan, S. H., & McElroy, T. (2012). Economic Time Series : Modeling and Seasonality. Boca Raton, FL: Chapman and Hall/CRC. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=445858