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    Post Closed as "primarily opinion-based" by Stephan Kolassa, Michael M, forecaster, Michael Chernick, Siong Thye Goh
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I usually teach finance (asset pricing and equilibrium models), quantitative economics (linear algebra and optimization), econometrics, computer science introduction to programming and machine learning (Bishop and Bengio). I am preparing a 60-hour introductory course of time series for PHD students. I would like to mix theoretical and computational issues. Although the student can use any computer language he wants, I will provide all the examples in python.

There are some expected topics that cannot disapeardisappear from any time series introductory course for a department of economics:

1) Arma models

2) Prediction and impulse response funcions based on ARMA models

3) Representations (covariance, wold and spectral)

4) Unit roots

5) Cointegration

  1. ARMA models

  2. Prediction and impulse response functions based on ARMA models

  3. Representations (covariance, Wold and spectral)

  4. Unit roots

  5. Cointegration

However, there are others that I am not exactly sure what to include.

6) Forecast (machine learning approach?)

6) Multivariate models (VAR?)

7) Nonlinear models (??) [TAR (?), Markov Switching (?), Garch Models (?)]

8) Topics of Monte Carlo and Bootstrapping for time series (?)

9) Contemporary issues? State of Art issues?:

10) Correct balance between theory and computation.

  1. Forecasting (machine learning approach?)

  2. Multivariate models (VAR?)

  3. Nonlinear models (??) [TAR (?), Markov Switching (?), Garch Models (?)]

  4. Topics of Monte Carlo and Bootstrapping for time series (?)

  5. Contemporary issues? State of Art issues?

  6. Correct balance between theory and computation.

Can you help me suggesting topics (and references for these topics) that I could include in the course? Are there time-series references such as Wooldridge (Introductory Econometrics: A Modern Approach) or Inzenman (Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning) that use real data that can be used to exemplify the models?

I usually teach finance (asset pricing and equilibrium models), quantitative economics (linear algebra and optimization), econometrics, computer science introduction to programming and machine learning (Bishop and Bengio). I am preparing a 60-hour introductory course of time series for PHD students. I would like to mix theoretical and computational issues. Although the student can use any computer language he wants, I will provide all the examples in python.

There are some expected topics that cannot disapear from any time series introductory course for a department of economics:

1) Arma models

2) Prediction and impulse response funcions based on ARMA models

3) Representations (covariance, wold and spectral)

4) Unit roots

5) Cointegration

However, there are others that I am not exactly sure what to include.

6) Forecast (machine learning approach?)

6) Multivariate models (VAR?)

7) Nonlinear models (??) [TAR (?), Markov Switching (?), Garch Models (?)]

8) Topics of Monte Carlo and Bootstrapping for time series (?)

9) Contemporary issues? State of Art issues?

10) Correct balance between theory and computation.

Can you help me suggesting topics (and references for these topics) that I could include in the course? Are there time-series references such as Wooldridge (Introductory Econometrics: A Modern Approach) or Inzenman (Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning) that use real data that can be used to exemplify the models?

I usually teach finance (asset pricing and equilibrium models), quantitative economics (linear algebra and optimization), econometrics, computer science introduction to programming and machine learning (Bishop and Bengio). I am preparing a 60-hour introductory course of time series for PHD students. I would like to mix theoretical and computational issues. Although the student can use any computer language he wants, I will provide all the examples in python.

There are some expected topics that cannot disappear from any time series introductory course for a department of economics:

  1. ARMA models

  2. Prediction and impulse response functions based on ARMA models

  3. Representations (covariance, Wold and spectral)

  4. Unit roots

  5. Cointegration

However, there are others that I am not exactly sure what to include:

  1. Forecasting (machine learning approach?)

  2. Multivariate models (VAR?)

  3. Nonlinear models (??) [TAR (?), Markov Switching (?), Garch Models (?)]

  4. Topics of Monte Carlo and Bootstrapping for time series (?)

  5. Contemporary issues? State of Art issues?

  6. Correct balance between theory and computation.

Can you help me suggesting topics (and references for these topics) that I could include in the course? Are there time-series references such as Wooldridge (Introductory Econometrics: A Modern Approach) or Inzenman (Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning) that use real data that can be used to exemplify the models?

    Post Made Community Wiki by whuber
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I usually teach finance (asset pricing and equilibrium models), quantitative economics (linear algebra and optimization), econometrics, computer science introduction to programming and machine learning (Bishop and Bengio). I am preparing a 60-hour introductory course of time series for PHD students. I would like to mix theoretical and computational issues. Although the student can use any computer language he wants, I will provide all the examples in python.

There are some expected topics that cannot disapear from any time series introductory course for a department of economics:

1) Arma models

2) Prediction and impulse response funcions based on ARMA models

3) Representations (covariance, wold and spectral)

4) Unit roots

5) Cointegration

However, there are others that I am not exactly sure what to include.

6) Forecast (machine learning approach?)

6) Multivariate models (VAR?)

7) Nonlinear models (??) [TAR (?), Markov Switching (?), Garch Models (?)]

8) Topics of Monte Carlo and Bootstrapping for time series (?)

9) Contemporary issues? State of Art issues?

10) Correct balance between theory and computation.

Can you help me suggesting topicssuggesting topics (and references for these topics) that I could include in the course? Are there time-series referencesAre there time-series references such as Wooldridge (Introductory Econometrics: A Modern Approach) or Inzenman (Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning) that use greatreal data that can be used to introduceexemplify the theorymodels?

I usually teach finance (asset pricing and equilibrium models), quantitative economics (linear algebra and optimization), econometrics, computer science introduction to programming and machine learning (Bishop and Bengio). I am preparing a 60-hour introductory course of time series for PHD students. I would like to mix theoretical and computational issues. Although the student can use any computer language he wants, I will provide all the examples in python.

There are some expected topics that cannot disapear from any time series introductory course for a department of economics:

1) Arma models

2) Prediction and impulse response funcions based on ARMA models

3) Representations (covariance, wold and spectral)

4) Unit roots

5) Cointegration

However, there are others that I am not exactly sure what to include.

6) Forecast (machine learning approach?)

6) Multivariate models (VAR?)

7) Nonlinear models (??) [TAR (?), Markov Switching (?), Garch Models (?)]

8) Topics of Monte Carlo and Bootstrapping for time series (?)

9) Contemporary issues? State of Art issues?

10) Correct balance between theory and computation.

Can you help me suggesting topics (and references for these topics) that I could include in the course? Are there time-series references such as Wooldridge (Introductory Econometrics: A Modern Approach) or Inzenman (Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning) that use great data to introduce the theory?

I usually teach finance (asset pricing and equilibrium models), quantitative economics (linear algebra and optimization), econometrics, computer science introduction to programming and machine learning (Bishop and Bengio). I am preparing a 60-hour introductory course of time series for PHD students. I would like to mix theoretical and computational issues. Although the student can use any computer language he wants, I will provide all the examples in python.

There are some expected topics that cannot disapear from any time series introductory course for a department of economics:

1) Arma models

2) Prediction and impulse response funcions based on ARMA models

3) Representations (covariance, wold and spectral)

4) Unit roots

5) Cointegration

However, there are others that I am not exactly sure what to include.

6) Forecast (machine learning approach?)

6) Multivariate models (VAR?)

7) Nonlinear models (??) [TAR (?), Markov Switching (?), Garch Models (?)]

8) Topics of Monte Carlo and Bootstrapping for time series (?)

9) Contemporary issues? State of Art issues?

10) Correct balance between theory and computation.

Can you help me suggesting topics (and references for these topics) that I could include in the course? Are there time-series references such as Wooldridge (Introductory Econometrics: A Modern Approach) or Inzenman (Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning) that use real data that can be used to exemplify the models?

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I usually teach finance (asset pricing and equilibrium models), quantitative economics (linear algebra and optimization), econometrics, computer science introduction to programming and machine learning (Bishop and Bengio). I am preparing a 60-hour introductory course of time series for PHD students. I would like to mix theoretical and computational issues. Although the student can use any computer language he wants, I will provide all the examples in python.

There are some expected topics that cannot disapear from any time series introductory course for a department of economics:

1) Arma models

2) Prediction and impulse response funcions based on ARMA models

3) Representations (covariance, wold and spectral)

4) Unit roots

5) Cointegration

However, there are others that I am not exactly sure what to include.

6) Forecast (machine learning approach?)

6) Multivariate models (VAR?)

7) Nonlinear models (??) [TAR (?), Markov Switching (?), Garch Models (?)]

8) Topics of Monte Carlo and Bootstrapping for time series (?)

9) Contemporary issues? State of Art issues?

10) Correct balance between theory and computation.

Can you help me suggesting topics (and references for these topics) that I could include in the course.? Are there time-series references such as Wooldridge (Introductory Econometrics: A Modern Approach) or Inzenman (Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning) that use great data to introduce the theory?

I usually teach finance (asset pricing and equilibrium models), quantitative economics (linear algebra and optimization), econometrics, computer science introduction to programming and machine learning (Bishop and Bengio). I am preparing a 60-hour introductory course of time series for PHD students. I would like to mix theoretical and computational issues. Although the student can use any computer language he wants, I will provide all the examples in python.

There are some expected topics that cannot disapear from any time series introductory course for a department of economics:

1) Arma models

2) Prediction and impulse response funcions based on ARMA models

3) Representations (covariance, wold and spectral)

4) Unit roots

5) Cointegration

However, there are others that I am not exactly sure what to include.

6) Forecast (machine learning approach?)

6) Multivariate models (VAR?)

7) Nonlinear models (??) [TAR (?), Markov Switching (?)]

8) Topics of Monte Carlo and Bootstrapping for time series (?)

9) Contemporary issues? State of Art issues?

10) Correct balance between theory and computation.

Can you help me suggesting topics (and references for these topics) that I could include in the course.

I usually teach finance (asset pricing and equilibrium models), quantitative economics (linear algebra and optimization), econometrics, computer science introduction to programming and machine learning (Bishop and Bengio). I am preparing a 60-hour introductory course of time series for PHD students. I would like to mix theoretical and computational issues. Although the student can use any computer language he wants, I will provide all the examples in python.

There are some expected topics that cannot disapear from any time series introductory course for a department of economics:

1) Arma models

2) Prediction and impulse response funcions based on ARMA models

3) Representations (covariance, wold and spectral)

4) Unit roots

5) Cointegration

However, there are others that I am not exactly sure what to include.

6) Forecast (machine learning approach?)

6) Multivariate models (VAR?)

7) Nonlinear models (??) [TAR (?), Markov Switching (?), Garch Models (?)]

8) Topics of Monte Carlo and Bootstrapping for time series (?)

9) Contemporary issues? State of Art issues?

10) Correct balance between theory and computation.

Can you help me suggesting topics (and references for these topics) that I could include in the course? Are there time-series references such as Wooldridge (Introductory Econometrics: A Modern Approach) or Inzenman (Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning) that use great data to introduce the theory?

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