Advanced Time Series Analysis

(18 Einträge)

Lecture Advanced Time Series Analysis, 1. Lesson

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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