Advanced Time Series Analysis
(56 Einträge)
Lecture Advanced Time Series Analysis, 1. Lesson
| Title: | Lecture Advanced Time Series Analysis, 1. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Montag, 02. November 2020 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2020-11-02 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20201102_001_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 2. Lesson
| Title: | Lecture Advanced Time Series Analysis, 2. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Montag, 02. November 2020 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2020-11-02 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20201102_002_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 3. Lesson
| Title: | Lecture Advanced Time Series Analysis, 3. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Freitag, 06. November 2020 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2020-11-06 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20201106_001_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 4. Lesson
| Title: | Lecture Advanced Time Series Analysis, 4. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Freitag, 06. November 2020 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2020-11-06 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20201106_002_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 5. Lesson
| Title: | Lecture Advanced Time Series Analysis, 5. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Montag, 09. November 2020 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2020-11-09 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20201109_001_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 6. Lesson
| Title: | Lecture Advanced Time Series Analysis, 6. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Montag, 09. November 2020 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2020-11-09 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20201109_002_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 7. Lesson
| Title: | Lecture Advanced Time Series Analysis, 7. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Freitag, 13. November 2020 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2020-11-13 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20201113_001_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 8. Lesson
| Title: | Lecture Advanced Time Series Analysis, 8. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Freitag, 13. November 2020 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2020-11-13 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20201113_002_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 9. Lesson
| Title: | Lecture Advanced Time Series Analysis, 9. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Montag, 16. November 2020 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2020-11-16 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20201116_001_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 10. Lesson
| Title: | Lecture Advanced Time Series Analysis, 10. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Montag, 16. November 2020 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2020-11-16 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20201116_002_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 11. Lesson
| Title: | Lecture Advanced Time Series Analysis, 11. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Freitag, 20. November 2020 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2020-11-20 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20201120_001_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 12. Lesson
| Title: | Lecture Advanced Time Series Analysis, 12. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Freitag, 20. November 2020 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2020-11-20 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20201120_002_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 13. Lesson
| Title: | Lecture Advanced Time Series Analysis, 13. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Montag, 23. November 2020 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2020-11-23 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20201123_001_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 14. Lesson
| Title: | Lecture Advanced Time Series Analysis, 14. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Montag, 23. November 2020 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2020-11-23 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20201123_002_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 15. Lesson
| Title: | Lecture Advanced Time Series Analysis, 15. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Freitag, 27. November 2020 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2020-11-27 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20201127_001_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 16. Lesson
| Title: | Lecture Advanced Time Series Analysis, 16. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Freitag, 27. November 2020 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2020-11-27 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20201127_002_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 17. Lesson
| Title: | Lecture Advanced Time Series Analysis, 17. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Montag, 30. November 2020 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2020-11-30 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20201130_001_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 18. Lesson
| Title: | Lecture Advanced Time Series Analysis, 18. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Montag, 30. November 2020 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2020-11-30 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20201130_002_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 19. Lesson
| Title: | Lecture Advanced Time Series Analysis, 19. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Freitag, 04. Dezember 2020 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2020-12-04 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20201204_001_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 20. Lesson
| Title: | Lecture Advanced Time Series Analysis, 20. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Freitag, 04. Dezember 2020 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2020-12-04 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20201204_002_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 21. Lesson
| Title: | Lecture Advanced Time Series Analysis, 21. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Montag, 07. Dezember 2020 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2020-12-07 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20201207_001_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 22. Lesson
| Title: | Lecture Advanced Time Series Analysis, 22. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Montag, 07. Dezember 2020 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2020-12-07 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20201207_002_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 23. Lesson
| Title: | Lecture Advanced Time Series Analysis, 23. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Freitag, 11. Dezember 2020 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2020-12-11 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20201211_001_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 24. Lesson
| Title: | Lecture Advanced Time Series Analysis, 24. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Freitag, 11. Dezember 2020 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2020-12-11 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20201211_002_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 25. Lesson
| Title: | Lecture Advanced Time Series Analysis, 25. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Montag, 14. Dezember 2020 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2020-12-14 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20201214_001_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 26. Lesson
| Title: | Lecture Advanced Time Series Analysis, 26. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Montag, 14. Dezember 2020 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2020-12-14 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20201214_002_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 27. Lesson
| Title: | Lecture Advanced Time Series Analysis, 27. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Freitag, 18. Dezember 2020 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2020-12-18 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20201218_001_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 28. Lesson
| Title: | Lecture Advanced Time Series Analysis, 28. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Freitag, 18. Dezember 2020 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2020-12-18 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20201218_002_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 29. Lesson
| Title: | Lecture Advanced Time Series Analysis, 29. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Montag, 11. Januar 2021 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2021-01-11 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20210111_001_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 30. Lesson
| Title: | Lecture Advanced Time Series Analysis, 30. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Montag, 11. Januar 2021 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2021-01-11 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20210111_002_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 31. Lesson
| Title: | Lecture Advanced Time Series Analysis, 31. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Freitag, 15. Januar 2021 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2021-01-15 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20210115_001_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 32. Lesson
| Title: | Lecture Advanced Time Series Analysis, 32. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Freitag, 15. Januar 2021 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2021-01-15 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20210115_002_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 33. Lesson
| Title: | Lecture Advanced Time Series Analysis, 33. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Montag, 18. Januar 2021 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2021-01-18 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20210118_001_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 34. Lesson
| Title: | Lecture Advanced Time Series Analysis, 34. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Montag, 18. Januar 2021 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2021-01-18 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20210118_002_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 35. Lesson
| Title: | Lecture Advanced Time Series Analysis, 35. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Freitag, 22. Januar 2021 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2021-01-22 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20210122_001_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 36. Lesson
| Title: | Lecture Advanced Time Series Analysis, 36. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Freitag, 22. Januar 2021 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2021-01-22 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20210122_002_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 37. Lesson
| Title: | Lecture Advanced Time Series Analysis, 37. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Montag, 25. Januar 2021 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2021-01-25 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20210125_001_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 38. Lesson
| Title: | Lecture Advanced Time Series Analysis, 38. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Montag, 25. Januar 2021 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2021-01-25 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20210125_002_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 39. Lesson
| Title: | Lecture Advanced Time Series Analysis, 39. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Freitag, 29. Januar 2021 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2021-01-29 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20210129_001_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 40. Lesson
| Title: | Lecture Advanced Time Series Analysis, 40. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Freitag, 29. Januar 2021 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2021-01-29 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20210129_002_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 41. Lesson
| Title: | Lecture Advanced Time Series Analysis, 41. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Montag, 01. Februar 2021 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2021-02-01 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20210201_001_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 42. Lesson
| Title: | Lecture Advanced Time Series Analysis, 42. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Montag, 01. Februar 2021 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2021-02-01 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20210201_002_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 43. Lesson
| Title: | Lecture Advanced Time Series Analysis, 43. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Freitag, 05. Februar 2021 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2021-02-05 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20210205_001_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 44. Lesson
| Title: | Lecture Advanced Time Series Analysis, 44. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Freitag, 05. Februar 2021 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2021-02-05 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20210205_002_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 45. Lesson
| Title: | Lecture Advanced Time Series Analysis, 45. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Montag, 08. Februar 2021 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2021-02-08 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20210208_001_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 46. Lesson
| Title: | Lecture Advanced Time Series Analysis, 46. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Montag, 08. Februar 2021 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2021-02-08 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20210208_002_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 47. Lesson
| Title: | Lecture Advanced Time Series Analysis, 47. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Freitag, 12. Februar 2021 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2021-02-12 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20210212_001_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 48. Lesson
| Title: | Lecture Advanced Time Series Analysis, 48. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Freitag, 12. Februar 2021 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2021-02-12 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20210212_002_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 49. Lesson
| Title: | Lecture Advanced Time Series Analysis, 49. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Montag, 15. Februar 2021 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2021-02-15 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20210215_001_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 50. Lesson
| Title: | Lecture Advanced Time Series Analysis, 50. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Montag, 15. Februar 2021 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2021-02-15 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20210215_002_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 51. Lesson
| Title: | Lecture Advanced Time Series Analysis, 51. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Freitag, 19. Februar 2021 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2021-02-19 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20210219_001_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 52. Lesson
| Title: | Lecture Advanced Time Series Analysis, 52. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Freitag, 19. Februar 2021 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2021-02-19 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20210219_002_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 53. Lesson
| Title: | Lecture Advanced Time Series Analysis, 53. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Montag, 22. Februar 2021 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2021-02-22 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20210222_001_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 54. Lesson
| Title: | Lecture Advanced Time Series Analysis, 54. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Montag, 22. Februar 2021 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2021-02-22 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20210222_002_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 55. Lesson
| Title: | Lecture Advanced Time Series Analysis, 55. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Freitag, 26. Februar 2021 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2021-02-26 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20210226_001_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
Lecture Advanced Time Series Analysis, 56. Lesson
| Title: | Lecture Advanced Time Series Analysis, 56. Lesson |
| Description: | Vorlesung im WiSe 2020-2021; Freitag, 26. Februar 2021 |
| Creator: | Joachim Grammig (author) |
| Contributor: | ZDV Universität Tübingen (producer) |
| Publisher: | ZDV Universität Tübingen |
| Date Created: | 2021-02-26 |
| Subjects: | Wirtschaftswissenschaft, Advanced Time Series Analysis, |
| Identifier: | UT_20210226_002_ws2021atsa_0001 |
| Rights: | Rechtshinweise |
| Abstracts: | Students master state-of-the-art time series econometrics, both univariate and multivariate. They apply time series methods with awareness of their potential and limitations in macroeconomics and finance. They command an econometric programming language independently and productively to perform empirical analyses involving time series data. They present and discuss their results of the application of time series methods in a scientific fashion. The module deals with a rigorous treatment of state-of-the art univariate and multivariate time series methods used in economics and finance. This includes: 1. Autoregressive moving average models 2. Forecasting 3. Regression analysis with stationary and non-stationary time series 4. Unit root tests 5. Structural vector-autoregressive models and cointegration 6. Equilibrium correction and Johansen methodology 7. Amplification of time series methods in macroeconomics and finance using econometric software 8. Conditional heteroskedasticity in financial time series |
