Facts about the course

ECTS Credits:
2.5
Responsible department:
Faculty of Logistics
Course Leader:
Tassew Dufera Tolcha
Lecture Semester:
Autumn
Duration:
1 week

LOG904-173 Introduction to Time Series Analysis (Autumn 2021)

About the course

The course introduces methods of time series analysis, building upon students’ background knowledge in statistical inference and regression analysis. The course begins with basic descriptive methods for viewing time series data and then talk about stationarity assumptions and how violations of these assumptions threaten inferences in regression analyses of time series data. Students will also learn about autoregressive integrated moving average (ARIMA) models, including autocorrelation (ACF) and partial autocorrelation (PACF) functions. The course will review several statistical tests for unit roots, serial correlation, and normality. Students will be introduced to regression-based time series models, such as the autoregressive distributed lag (ADL) model, as well as more advanced models such as the error correction and GARCH models. Students will also learn about modeling interventions in time series data.

Topics covered: Introduction to Time Series Analysis; ARIMA Models; Regression/Intervention Analysis & Structural Breaks; Granger Causality and ARCH/GARCH Models; Cointegration and Error Correction Models.

The course is connected to the following study programs

Recommended requirements

There is no requirement of previous knowledge of the topic. But being familiar with basic regression analysis and the fundamentals of statistical inference is recommended. STATA will be used in the course but no need of prior knowledge of it. The necessary codes and time series datasets will be provided.

Forms of teaching and learning

Three hours of lectures in the morning are used to discuss concepts and to present their implementation/analysis in STATA. In the afternoon, the students are required to work on exercises/assignments in the computer lab and under the supervision of the lecturer.

Examination

Form of assessment: Home assessment without presentation 1

  • Proportion: 40%

  • Duration: -

  • Grouping: Individual

  • Grading scale: Letter (A - F)

 


Form of assessment: Home assessment without presentation 2

  • Proportion: 60%

  • Duration: -

  • Grouping: Individual

  • Grading scale: Letter (A - F)


Syllabus

Recommended Text:

Box-Steffensmeier, Janet M., John R. Freeman, Matthew P. Hitt, and Jon C.W. Pevehouse. 2014. Time Series Analysis for the Social Sciences. Cambridge University Press.

Last updated from FS (Common Student System) Oct. 24, 2021 8:20:26 AM