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14.384 Time Series Analysis (MIT) 14.384 Time Series Analysis (MIT)

Description

Subjects

univariate stationary | univariate stationary | univariate non-stationary | univariate non-stationary | vector autoregressions | vector autoregressions | frequency domain analysis | frequency domain analysis | persistent time series | persistent time series | structural breaks | structural breaks | dynamic stochastic general equilibrium | dynamic stochastic general equilibrium | DSGE | DSGE | Bayesian | Bayesian | econometrics | econometrics | VAR | VAR | unit root | unit root | prediction regression | prediction regression | GMM | GMM | MCMC | MCMC

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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14.384 Time Series Analysis (MIT) 14.384 Time Series Analysis (MIT)

Description

The course provides a survey of the theory and application of time series methods in econometrics. Topics covered will include univariate stationary and non-stationary models, vector autoregressions, frequency domain methods, models for estimation and inference in persistent time series, and structural breaks. We will cover different methods of estimation and inferences of modern dynamic stochastic general equilibrium models (DSGE): simulated method of moments, maximum likelihood and Bayesian approach. The empirical applications in the course will be drawn primarily from macroeconomics. The course provides a survey of the theory and application of time series methods in econometrics. Topics covered will include univariate stationary and non-stationary models, vector autoregressions, frequency domain methods, models for estimation and inference in persistent time series, and structural breaks. We will cover different methods of estimation and inferences of modern dynamic stochastic general equilibrium models (DSGE): simulated method of moments, maximum likelihood and Bayesian approach. The empirical applications in the course will be drawn primarily from macroeconomics.

Subjects

univariate stationary | univariate stationary | univariate non-stationary | univariate non-stationary | vector autoregressions | vector autoregressions | frequency domain analysis | frequency domain analysis | persistent time series | persistent time series | structural breaks | structural breaks | dynamic stochastic general equilibrium | dynamic stochastic general equilibrium | DSGE | DSGE | Bayesian | Bayesian | econometrics | econometrics | VAR | VAR | unit root | unit root | prediction regression | prediction regression | GMM | GMM | MCMC | MCMC

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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14.384 Time Series Analysis (MIT)

Description

The course provides a survey of the theory and application of time series methods in econometrics. Topics covered will include univariate stationary and non-stationary models, vector autoregressions, frequency domain methods, models for estimation and inference in persistent time series, and structural breaks. We will cover different methods of estimation and inferences of modern dynamic stochastic general equilibrium models (DSGE): simulated method of moments, maximum likelihood and Bayesian approach. The empirical applications in the course will be drawn primarily from macroeconomics.

Subjects

univariate stationary | univariate non-stationary | vector autoregressions | frequency domain analysis | persistent time series | structural breaks | dynamic stochastic general equilibrium | DSGE | Bayesian | econometrics | VAR | unit root | prediction regression | GMM | MCMC

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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Getting it right at the start (Excellence in Practice)

Description

John McNair, Head of Business and Community Development, Cumbernauld College

Subjects

Excellence in Practice | pre-induction engagement | MCMC | webinar | hairdressing and beauty | business | management and administration | AY : Office Skills | HL : Hair/Personal Care Services | GB : Teaching/Training | SCQF Level 1

License

Attribution-NonCommercial-ShareAlike 3.0 Unported Attribution-NonCommercial-ShareAlike 3.0 Unported http://creativecommons.org/licenses/by-nc-sa/3.0/ http://creativecommons.org/licenses/by-nc-sa/3.0/ Copyright Scotland's Colleges Copyright Scotland's Colleges

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14.384 Time Series Analysis (MIT)

Description

The course provides a survey of the theory and application of time series methods in econometrics. Topics covered will include univariate stationary and non-stationary models, vector autoregressions, frequency domain methods, models for estimation and inference in persistent time series, and structural breaks. We will cover different methods of estimation and inferences of modern dynamic stochastic general equilibrium models (DSGE): simulated method of moments, maximum likelihood and Bayesian approach. The empirical applications in the course will be drawn primarily from macroeconomics.

Subjects

univariate stationary | univariate non-stationary | vector autoregressions | frequency domain analysis | persistent time series | structural breaks | dynamic stochastic general equilibrium | DSGE | Bayesian | econometrics | VAR | unit root | prediction regression | GMM | MCMC

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

Site sourced from

https://ocw.mit.edu/rss/all/mit-allarchivedcourses.xml

Attribution

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14.384 Time Series Analysis (MIT)

Description

The course provides a survey of the theory and application of time series methods in econometrics. Topics covered will include univariate stationary and non-stationary models, vector autoregressions, frequency domain methods, models for estimation and inference in persistent time series, and structural breaks. We will cover different methods of estimation and inferences of modern dynamic stochastic general equilibrium models (DSGE): simulated method of moments, maximum likelihood and Bayesian approach. The empirical applications in the course will be drawn primarily from macroeconomics.Recommended CitationFor any use or distribution of these materials, please cite as follows:Anna Mikusheva, course materials for 14.384 Time Series Analysis, Fall 2007. MIT OpenCourseWare (http://ocw.mit.edu)

Subjects

univariate stationary | univariate non-stationary | vector autoregressions | frequency domain analysis | persistent time series | structural breaks | dynamic stochastic general equilibrium | DSGE | Bayesian | econometrics | VAR | unit root | prediction regression | GMM | MCMC

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

Site sourced from

https://ocw.mit.edu/rss/all/mit-allarchivedcourses.xml

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