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Detailinformationen |
Quellcurriculum |
Masterstudium Economic and Business Analytics 2025W |
Lernergebnisse |
Kompetenzen |
(*)Students are able to specify, estimate, and test econometric models for analyzing, forecasting, and testing hypothesis on financial and macroeconomic time series. Students are able to write and use estimation and test routines in MATLAB, R, or Python and interpret the empirical results.
Course Goals
Course Goals
The goal of this course is to introduce students to the tools of time series econometrics and enable them to tackle current problems in empirical finance and macroeconomics. We start with a brief refresher of the theory and solution of first- and second-order difference equations, which helps understanding the dynamic behavior of financial and macroeconomic time series. We then discuss the concept of a likelihood function and Maximum Likelihood estimation (MLE) for time series.
The main part of the course covers the specification, estimation, and evaluation of stationary and nonstationary univariate time series models with homoskedastic and heteroskedastic error terms. Given sufficient time, we also cover multivariate time series models, such as vector-autoregressive (VAR) models, the concept of co-integration, and vector error-correction (VEC) models.
This course is applied in the sense that students must solve practical homework assignments that involve coding statistical tests and estimation routines in MATLAB, R, or Python. The course is intended for students in the Master’s degree programs in Economics and Economic and Business
Analytics. Students with a specialization in finance, statistics, or related fields are also welcome.
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Fertigkeiten |
Kenntnisse |
(*)- Learning Outcome 2 (LO2): Understand the properties of different time series models and their usability for a financial or macroeconomic variable of interest.
- Learning Outcome 3 (LO3): Apply the theoretical knowledge to specify, estimate and test univariate time series models on financial and macroeconomic variables.
- Learning Outcome 4 (LO4): Analyze and interpret the empirical results about the in-sample fit, out-of-sample forecasting properties, and the testing of theoretical hypotheses.
- Learning Outcome 5 (LO5): Write, adapt, and use estimation and test routines in MATLAB, R, or Python.
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(*)- Learning Outcome 1 (LO1): Recall the theory and solution of first- and second-order difference equations.
Course Content
- Introduction and Background
- The Concept of Time Series
- Difference Equations and their Solutions
- Maximum Likelihood Estimation
- Univariate Time Series Models
- Stationary Time Series Model
- Deterministic and Stochastic Trends
- Modeling Time-Varying Volatility
- Multivariate Time Series Models
- Vector-Autoregression (VAR) Models
- Co-integration and Vector Error-Correction (VEC) Models
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Beurteilungskriterien |
(*)The final grade in this course is based on the homework assignments (50%) and the written exam (50%). The homework assignments, the final exam, and the retake exam are all graded based on the following grading scheme:
Percent | Grade |
85 - 100 | 1 |
70 - 84 | 2 |
55 - 69,5 | 3 |
40 - 54,5 | 4 |
0 - 39,5 | 5 |
- Homework assignments: There are 2 or more homework assignments, which count equally towards the final grade in this course. Homework assignments include both paper & pencil derivations of theoretical results and practical applications, where students must write and apply estimation and test routines in MATLAB, R, or Python to simulated or empirical time series. Homework assignments may be solved individually or in small groups of up to 2 students.
- Written exam: The final and retake exam is closed-book (i.e. no course material or other aids allowed), must be solved individually, and comprises of theoretical derivations and interpretations of empirical estimation or test results based on theoretical and empirical times series.
Synchronization of learning outcomes and assessments:
- LO1: Written exam(s) & Homework assignments
- LO2: Written exam(s) & Homework assignments
- LO3: Written exam(s) & Homework assignments
- LO4: Written exam(s) & Homework assignments
- LO5: Homework assignments
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Lehrmethoden |
(*)This course combines different teaching and learning methods in order to
- maximize students’ understanding of the concept of time series and econometric tools,
- convey the intuition and interpretation of empirical results for exemplary financial and macroeconomic variables,
- encourage students to practice their analytical skills and apply the econometric tools learned in the course.
This includes the following:
- Teacher-centered lectures based on slides and a reference textbook
- Step-by-step derivation of key formal and graphical results on the blackboard
- Homework assignments
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Abhaltungssprache |
Englisch |
Literatur |
(*)- Lecture slides (containing examples and links to economic databases)
- Homework assignments & solutions (including MATLAB and R code)
- Selected textbook chapters from: Enders, Walter (2010). Applied Econometric Time Series. 3rd edition (4th edition, 2014), John Wiley & Sons.
(Lecture slides, homework assignments & solutions are made available via KUSSS)
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Lehrinhalte wechselnd? |
Nein |
Gilt als absolviert, wenn |
(*)977ANMEFAMK22: KS Financial and Macroeconometrics (4ECTS);
971ATECFAMK19: KS Financial and Macroeconometrics (4ECTS)
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