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

Description

The course is an introduction to univariate and multivariate time series models. It starts by introducing basic concepts and progresses to more complicated models. The course intends to meet two goals. It provides tools for empirical work with time series data and is an introduction into the theoretical foundation of time series models. The course is an introduction to univariate and multivariate time series models. It starts by introducing basic concepts and progresses to more complicated models. The course intends to meet two goals. It provides tools for empirical work with time series data and is an introduction into the theoretical foundation of time series models.Subjects

time series analysis | time series analysis | univariate time series model | univariate time series model | multivariate time series model | multivariate time series model | time series model | time series modelLicense

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.htmSite sourced from

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This course is a student-presented seminar in combinatorics, graph theory, and discrete mathematics in general. Instruction and practice in written and oral communication is emphasized, with participants reading and presenting papers from recent mathematics literature and writing a final paper in a related topic. This course is a student-presented seminar in combinatorics, graph theory, and discrete mathematics in general. Instruction and practice in written and oral communication is emphasized, with participants reading and presenting papers from recent mathematics literature and writing a final paper in a related topic.Subjects

discrete math; discrete mathematics; discrete; math; mathematics; seminar; presentations; student presentations; oral; communication; stable marriage; dych; emergency; response vehicles; ambulance; game theory; congruences; color theorem; four color; cake cutting; algorithm; RSA; encryption; numberical integration; sorting; post correspondence problem; PCP; ramsey; van der waals; fibonacci; recursion; domino; tiling; towers; hanoi; pigeonhole; principle; matrix; hamming; code; hat game; juggling; zero-knowledge; proof; repeated games; lewis carroll; determinants; infinitude of primes; bridges; konigsberg; koenigsberg; time series analysis; GARCH; rational; recurrence; relations; digital; image; compression; quantum computing | discrete math; discrete mathematics; discrete; math; mathematics; seminar; presentations; student presentations; oral; communication; stable marriage; dych; emergency; response vehicles; ambulance; game theory; congruences; color theorem; four color; cake cutting; algorithm; RSA; encryption; numberical integration; sorting; post correspondence problem; PCP; ramsey; van der waals; fibonacci; recursion; domino; tiling; towers; hanoi; pigeonhole; principle; matrix; hamming; code; hat game; juggling; zero-knowledge; proof; repeated games; lewis carroll; determinants; infinitude of primes; bridges; konigsberg; koenigsberg; time series analysis; GARCH; rational; recurrence; relations; digital; image; compression; quantum computing | discrete math | discrete math | discrete mathematics | discrete mathematics | discrete | discrete | math | math | mathematics | mathematics | seminar | seminar | presentations | presentations | student presentations | student presentations | oral | oral | communication | communication | stable marriage | stable marriage | dych | dych | emergency | emergency | response vehicles | response vehicles | ambulance | ambulance | game theory | game theory | congruences | congruences | color theorem | color theorem | four color | four color | cake cutting | cake cutting | algorithm | algorithm | RSA | RSA | encryption | encryption | numberical integration | numberical integration | sorting | sorting | post correspondence problem | post correspondence problem | PCP | PCP | ramsey | ramsey | van der waals | van der waals | fibonacci | fibonacci | recursion | recursion | domino | domino | tiling | tiling | towers | towers | hanoi | hanoi | pigeonhole | pigeonhole | principle | principle | matrix | matrix | hamming | hamming | code | code | hat game | hat game | juggling | juggling | zero-knowledge | zero-knowledge | proof | proof | repeated games | repeated games | lewis carroll | lewis carroll | determinants | determinants | infinitude of primes | infinitude of primes | bridges | bridges | konigsberg | konigsberg | koenigsberg | koenigsberg | time series analysis | time series analysis | GARCH | GARCH | rational | rational | recurrence | recurrence | relations | relations | digital | digital | image | image | compression | compression | quantum computing | quantum computingLicense

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.htmSite sourced from

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See all metadata14.384 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

time series | time series | time series analysis | time series analysis | data sets | data sets | empirical research | empirical research | economics | economics | econometric | econometric | univariate stationary models | univariate stationary models | non-stationary models | non-stationary models | vector autoregressions | vector autoregressions | frequency domain methods | frequency domain methods | estimation | inference | estimation | inference | modern dynamic stochastic general equilibrium models | modern dynamic stochastic general equilibrium models | DGSE | DGSE | Maximum likelihood | Maximum likelihood | Bayesian | BayesianLicense

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.htmSite sourced from

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This course is a student-presented seminar in combinatorics, graph theory, and discrete mathematics in general. Instruction and practice in written and oral communication is emphasized, with participants reading and presenting papers from recent mathematics literature and writing a final paper in a related topic. This course is a student-presented seminar in combinatorics, graph theory, and discrete mathematics in general. Instruction and practice in written and oral communication is emphasized, with participants reading and presenting papers from recent mathematics literature and writing a final paper in a related topic.Subjects

discrete math; discrete mathematics; discrete; math; mathematics; seminar; presentations; student presentations; oral; communication; stable marriage; dych; emergency; response vehicles; ambulance; game theory; congruences; color theorem; four color; cake cutting; algorithm; RSA; encryption; numberical integration; sorting; post correspondence problem; PCP; ramsey; van der waals; fibonacci; recursion; domino; tiling; towers; hanoi; pigeonhole; principle; matrix; hamming; code; hat game; juggling; zero-knowledge; proof; repeated games; lewis carroll; determinants; infinitude of primes; bridges; konigsberg; koenigsberg; time series analysis; GARCH; rational; recurrence; relations; digital; image; compression; quantum computing | discrete math; discrete mathematics; discrete; math; mathematics; seminar; presentations; student presentations; oral; communication; stable marriage; dych; emergency; response vehicles; ambulance; game theory; congruences; color theorem; four color; cake cutting; algorithm; RSA; encryption; numberical integration; sorting; post correspondence problem; PCP; ramsey; van der waals; fibonacci; recursion; domino; tiling; towers; hanoi; pigeonhole; principle; matrix; hamming; code; hat game; juggling; zero-knowledge; proof; repeated games; lewis carroll; determinants; infinitude of primes; bridges; konigsberg; koenigsberg; time series analysis; GARCH; rational; recurrence; relations; digital; image; compression; quantum computing | discrete math | discrete math | discrete mathematics | discrete mathematics | discrete | discrete | math | math | mathematics | mathematics | seminar | seminar | presentations | presentations | student presentations | student presentations | oral | oral | communication | communication | stable marriage | stable marriage | dych | dych | emergency | emergency | response vehicles | response vehicles | ambulance | ambulance | game theory | game theory | congruences | congruences | color theorem | color theorem | four color | four color | cake cutting | cake cutting | algorithm | algorithm | RSA | RSA | encryption | encryption | numberical integration | numberical integration | sorting | sorting | post correspondence problem | post correspondence problem | PCP | PCP | ramsey | ramsey | van der waals | van der waals | fibonacci | fibonacci | recursion | recursion | domino | domino | tiling | tiling | towers | towers | hanoi | hanoi | pigeonhole | pigeonhole | principle | principle | matrix | matrix | hamming | hamming | code | code | hat game | hat game | juggling | juggling | zero-knowledge | zero-knowledge | proof | proof | repeated games | repeated games | lewis carroll | lewis carroll | determinants | determinants | infinitude of primes | infinitude of primes | bridges | bridges | konigsberg | konigsberg | koenigsberg | koenigsberg | time series analysis | time series analysis | GARCH | GARCH | rational | rational | recurrence | recurrence | relations | relations | digital | digital | image | image | compression | compression | quantum computing | quantum computingLicense

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.htmSite sourced from

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See all metadata14.384 Time Series Analysis (MIT)

Description

The course is an introduction to univariate and multivariate time series models. It starts by introducing basic concepts and progresses to more complicated models. The course intends to meet two goals. It provides tools for empirical work with time series data and is an introduction into the theoretical foundation of time series models.Subjects

time series analysis | univariate time series model | multivariate time series model | time series modelLicense

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.htmSite sourced from

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See all metadata12.740 Paleoceanography (MIT) 12.740 Paleoceanography (MIT)

Description

This class examines tools, data, and ideas related to past climate changes as seen in marine, ice core, and continental records. The most recent climate changes (mainly the past 500,000 years, ranging up to about 2 million years ago) will be emphasized. Quantitative tools for the examination of paleoceanographic data will be introduced (statistics, factor analysis, time series analysis, simple climatology). This class examines tools, data, and ideas related to past climate changes as seen in marine, ice core, and continental records. The most recent climate changes (mainly the past 500,000 years, ranging up to about 2 million years ago) will be emphasized. Quantitative tools for the examination of paleoceanographic data will be introduced (statistics, factor analysis, time series analysis, simple climatology).Subjects

history of the earth-surface environment | history of the earth-surface environment | deep-sea sediments | deep-sea sediments | ice cores | ice cores | corals | corals | Micropaleontological | Micropaleontological | isotopic | isotopic | geochemical | and mineralogical changes | geochemical | and mineralogical changes | seawater composition | seawater composition | atmospheric chemistry | atmospheric chemistry | climate | climate | ocean temperature | ocean temperature | circulation | circulation | chemistry | chemistry | glacial/interglacial cycles | glacial/interglacial cycles | orbital forcing | orbital forcing | climate change | climate change | marine records | marine records | ice core records | ice core records | continental records | continental records | paleoceanographic data | paleoceanographic data | statistics | statistics | factor analysis | factor analysis | time series analysis | time series analysis | simple climatology | simple climatology | geochemical changes | geochemical changes | mineralogical changes | mineralogical changes | glacial cycles | glacial cycles | intergalacial cycles | intergalacial cycles | earth-surface environment | earth-surface environment | environmental history | environmental history | Oxygen Isotope | Oxygen Isotope | Coral Reefs | Coral Reefs | Paleoceanography | Paleoceanography | Paleoclimatology | Paleoclimatology | Paleothermometry | Paleothermometry | Atmospheric Carbon Dioxide | Atmospheric Carbon Dioxide | Ocean Chemistry | Ocean Chemistry | Salinity | SalinityLicense

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.htmSite sourced from

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See all metadata14.384 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 | MCMCLicense

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.htmSite sourced from

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See all metadata12.740 Paleoceanography (MIT) 12.740 Paleoceanography (MIT)

Description

This class examines tools, data, and ideas related to past climate changes as seen in marine, ice core, and continental records. The most recent climate changes (mainly the past 500,000 years, ranging up to about 2 million years ago) will be emphasized. Quantitative tools for the examination of paleoceanographic data will be introduced (statistics, factor analysis, time series analysis, simple climatology). This class examines tools, data, and ideas related to past climate changes as seen in marine, ice core, and continental records. The most recent climate changes (mainly the past 500,000 years, ranging up to about 2 million years ago) will be emphasized. Quantitative tools for the examination of paleoceanographic data will be introduced (statistics, factor analysis, time series analysis, simple climatology).Subjects

history of the earth-surface environment | history of the earth-surface environment | deep-sea sediments | deep-sea sediments | ice cores | ice cores | corals | corals | Micropaleontological | Micropaleontological | isotopic | isotopic | geochemical | and mineralogical changes | geochemical | and mineralogical changes | seawater composition | seawater composition | atmospheric chemistry | atmospheric chemistry | climate | climate | ocean temperature | ocean temperature | circulation | circulation | chemistry | chemistry | glacial/interglacial cycles | glacial/interglacial cycles | orbital forcing | orbital forcing | climate change | climate change | marine records | marine records | ice core records | ice core records | continental records | continental records | paleoceanographic data | paleoceanographic data | statistics | statistics | factor analysis | factor analysis | time series analysis | time series analysis | simple climatology | simple climatology | geochemical changes | geochemical changes | mineralogical changes | mineralogical changes | glacial cycles | glacial cycles | intergalacial cycles | intergalacial cycles | earth-surface environment | earth-surface environment | environmental history | environmental historyLicense

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.htmSite sourced from

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See all metadata12.864 Inference from Data and Models (MIT) 12.864 Inference from Data and Models (MIT)

Description

The course is directed at making scientifically sensible deductions from the combination of observations with dynamics and kinematics represented, generically, as "models". There are two overlapping central themeslinear "inverse" methods and data "assimilation" including regression, singular value decomposition, objective mapping, non-stationary models and data, Kalman filters, adjoint methods ("assimilation") etc.standard time series analysis, including basic statistics, Fourier methods, spectra, coherence, filtering, etc. The course is directed at making scientifically sensible deductions from the combination of observations with dynamics and kinematics represented, generically, as "models". There are two overlapping central themeslinear "inverse" methods and data "assimilation" including regression, singular value decomposition, objective mapping, non-stationary models and data, Kalman filters, adjoint methods ("assimilation") etc.standard time series analysis, including basic statistics, Fourier methods, spectra, coherence, filtering, etc.Subjects

observation | observation | kinematical models | kinematical models | dynamical models | dynamical models | basic statistics | basic statistics | linear algebra | linear algebra | inverse methods | inverse methods | singular value decompositions | singular value decompositions | control theory | control theory | sequential estimation | sequential estimation | Kalman filters | Kalman filters | smoothing algorithms | smoothing algorithms | adjoint/Pontryagin principle methods | adjoint/Pontryagin principle methods | model testing | model testing | stationary processes | stationary processes | Fourier methods | Fourier methods | z-transforms | z-transforms | sampling theorems | sampling theorems | spectra | spectra | multi-taper methods | multi-taper methods | coherences | coherences | filtering | filtering | quantitative combinations | quantitative combinations | realistic observations | realistic observations | data assimilations | data assimilations | deduction | deduction | regression | regression | objective mapping | objective mapping | time series analysis | time series analysis | inference | inferenceLicense

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.htmSite sourced from

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See all metadata6.435 System Identification (MIT) 6.435 System Identification (MIT)

Description

This course is offered to graduates and includes topics such as mathematical models of systems from observations of their behavior; time series, state-space, and input-output models; model structures, parametrization, and identifiability; non-parametric methods; prediction error methods for parameter estimation, convergence, consistency, and asymptotic distribution; relations to maximum likelihood estimation; recursive estimation; relation to Kalman filters; structure determination; order estimation; Akaike criterion; bounded but unknown noise model; and robustness and practical issues. This course is offered to graduates and includes topics such as mathematical models of systems from observations of their behavior; time series, state-space, and input-output models; model structures, parametrization, and identifiability; non-parametric methods; prediction error methods for parameter estimation, convergence, consistency, and asymptotic distribution; relations to maximum likelihood estimation; recursive estimation; relation to Kalman filters; structure determination; order estimation; Akaike criterion; bounded but unknown noise model; and robustness and practical issues.Subjects

mathematical models | mathematical models | time series | time series | state-space | state-space | input-output models | input-output models | model structures | model structures | parametrization | parametrization | identifiability | identifiability | non-parametric methods | non-parametric methods | prediction error | prediction error | parameter estimation | parameter estimation | convergence | convergence | consistency | consistency | andasymptotic distribution | andasymptotic distribution | maximum likelihood estimation | maximum likelihood estimation | recursive estimation | recursive estimation | Kalman filters | Kalman filters | structure determination | structure determination | order estimation | order estimation | Akaike criterion | Akaike criterion | bounded noise models | bounded noise models | robustness | robustnessLicense

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.htmSite sourced from

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See all metadata12.740 Paleoceanography (MIT) 12.740 Paleoceanography (MIT)

Description

This class examines tools, data, and ideas related to past climate changes as seen in marine, ice core, and continental records. The most recent climate changes (mainly the past 500,000 years, ranging up to about 2 million years ago) will be emphasized. Quantitative tools for the examination of paleoceanographic data will be introduced (statistics, factor analysis, time series analysis, simple climatology). This class examines tools, data, and ideas related to past climate changes as seen in marine, ice core, and continental records. The most recent climate changes (mainly the past 500,000 years, ranging up to about 2 million years ago) will be emphasized. Quantitative tools for the examination of paleoceanographic data will be introduced (statistics, factor analysis, time series analysis, simple climatology).Subjects

history of the earth-surface environment | history of the earth-surface environment | deep-sea sediments | deep-sea sediments | ice cores | ice cores | corals | corals | Micropaleontological | Micropaleontological | isotopic | isotopic | geochemical | and mineralogical changes | geochemical | and mineralogical changes | seawater composition | seawater composition | atmospheric chemistry | atmospheric chemistry | climate | climate | ocean temperature | ocean temperature | circulation | circulation | chemistry | chemistry | glacial/interglacial cycles | glacial/interglacial cycles | orbital forcing | orbital forcing | climate change | climate change | marine records | marine records | ice core records | ice core records | continental records | continental records | paleoceanographic data | paleoceanographic data | statistics | statistics | factor analysis | factor analysis | time series analysis | time series analysis | simple climatology | simple climatology | geochemical changes | geochemical changes | mineralogical changes | mineralogical changes | glacial cycles | glacial cycles | intergalacial cycles | intergalacial cycles | earth-surface environment | earth-surface environment | environmental history | environmental history | Oxygen Isotope | Oxygen Isotope | Coral Reefs | Coral Reefs | Paleoceanography | Paleoceanography | Paleoclimatology | Paleoclimatology | Paleothermometry | Paleothermometry | Atmospheric Carbon Dioxide | Atmospheric Carbon Dioxide | Ocean Chemistry | Ocean Chemistry | Salinity | SalinityLicense

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.htmSite sourced from

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See all metadata14.384 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 | MCMCLicense

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.htmSite sourced from

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See all metadata18.304 Undergraduate Seminar in Discrete Mathematics (MIT)

Description

This course is a student-presented seminar in combinatorics, graph theory, and discrete mathematics in general. Instruction and practice in written and oral communication is emphasized, with participants reading and presenting papers from recent mathematics literature and writing a final paper in a related topic.Subjects

discrete math; discrete mathematics; discrete; math; mathematics; seminar; presentations; student presentations; oral; communication; stable marriage; dych; emergency; response vehicles; ambulance; game theory; congruences; color theorem; four color; cake cutting; algorithm; RSA; encryption; numberical integration; sorting; post correspondence problem; PCP; ramsey; van der waals; fibonacci; recursion; domino; tiling; towers; hanoi; pigeonhole; principle; matrix; hamming; code; hat game; juggling; zero-knowledge; proof; repeated games; lewis carroll; determinants; infinitude of primes; bridges; konigsberg; koenigsberg; time series analysis; GARCH; rational; recurrence; relations; digital; image; compression; quantum computing | discrete math | discrete mathematics | discrete | math | mathematics | seminar | presentations | student presentations | oral | communication | stable marriage | dych | emergency | response vehicles | ambulance | game theory | congruences | color theorem | four color | cake cutting | algorithm | RSA | encryption | numberical integration | sorting | post correspondence problem | PCP | ramsey | van der waals | fibonacci | recursion | domino | tiling | towers | hanoi | pigeonhole | principle | matrix | hamming | code | hat game | juggling | zero-knowledge | proof | repeated games | lewis carroll | determinants | infinitude of primes | bridges | konigsberg | koenigsberg | time series analysis | GARCH | rational | recurrence | relations | digital | image | compression | quantum computingLicense

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.htmSite sourced from

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See all metadata14.384 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

time series | time series analysis | data sets | empirical research | economics | econometric | univariate stationary models | non-stationary models | vector autoregressions | frequency domain methods | estimation | inference | modern dynamic stochastic general equilibrium models | DGSE | Maximum likelihood | BayesianLicense

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.htmSite sourced from

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See all metadata14.384 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 | MCMCLicense

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.htmSite sourced from

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See all metadataDescription

This class examines tools, data, and ideas related to past climate changes as seen in marine, ice core, and continental records. The most recent climate changes (mainly the past 500,000 years, ranging up to about 2 million years ago) will be emphasized. Quantitative tools for the examination of paleoceanographic data will be introduced (statistics, factor analysis, time series analysis, simple climatology).Subjects

history of the earth-surface environment | deep-sea sediments | ice cores | corals | Micropaleontological | isotopic | geochemical | and mineralogical changes | seawater composition | atmospheric chemistry | climate | ocean temperature | circulation | chemistry | glacial/interglacial cycles | orbital forcing | climate change | marine records | ice core records | continental records | paleoceanographic data | statistics | factor analysis | time series analysis | simple climatology | geochemical changes | mineralogical changes | glacial cycles | intergalacial cycles | earth-surface environment | environmental history | Oxygen Isotope | Coral Reefs | Paleoceanography | Paleoclimatology | Paleothermometry | Atmospheric Carbon Dioxide | Ocean Chemistry | SalinityLicense

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.htmSite sourced from

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See all metadataIntroduction to modelling for 1st year engineers

Description

These resources cover an introduction to modelling for first year engineering undergraduate. The main focus is on electrical and mechanical systems, but there is also some discussion of dc motors, fluids and heat as well as an introduction to time series modelling. The main emphasis is on why modelling is important and how to go about doing this from first principles (e.g. Kirchhoff's laws, Newton's Laws, etc.). Given the focus is on new students arriving at University, there is no attempt to develop models beyond second order. The resources here include the lecture hand out (pdf) which includes embedded tutorial questions, some powerpoints for structuring lectures , flash animations to step through modelling process for electrical circuits and a large data base of CAA developed on webctSubjects

modelling | mechanical systems | series components | parallel components | potential divider | fluid flow | heat flow | dc servos | time series | systems engineering | mass-spring-damper | engineering undergraduate education | creative commons | ukoer | electric circuits | oer | jisc | hea | hea engineering subject centre university of sheffield | sheffieldunioer | engscoer | cc-by | wales | engineering | Engineering | H000 | ENGINEERING | XLicense

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Introductory course on learning and using MATLAB aimed at 1st year engineering undergraduate. These were developed at the University of Sheffield and authored by J A Rossiter from The Department of Automatic Control and Systems Engineering. The files include a slightly animated power point slide (runs via web) which includes audio. Hence a little like a lecture. The source m-files mentioned are also supplied in the zip files mentioned. These m-files cover a group of topics. Read the instruction file to learn how to make 'lectures' available to students. More information for control with MATLAB is stored under the control contribution by same author and on the website http://controleducation.group.shef.ac.uk/acs211/notes_webct_quizzes_acs211.htmSubjects

engineering problem solving | symbolic toolbox | function files | optimisation | conditionnals | loops | time series | ukoer | m-files | engineering undergraduate education | control engineering | creative commons | animated powerpoint with voiceover | first year engineering | matlab | control with matlab | oer | jisc | hea | hea engineering subject centre | flash | university of sheffield | sheffieldunioer | engscoer | cc-by | wales | engineering | Mathematical and Computer Sciences | Engineering | Computer science | H000 | I100 | ENGINEERING | XLicense

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See all metadataMeasuring inequality: Using the Lorenz Curve and Gini Coefficient

Description

Part of a series of worksheets covering Mathematical Case Studies for Economists from Nottingham Trent University. They are downloadable in Word format with embedded links. They can be adapted, printed and/or put in a Virtual Learning Environment. A booklet giving guideline answers for the task questions is available on request from the Economics Network.Subjects

ukoer | trueproject | economics | mathematics | social issues | cross sectional | time series | summary statistic | percentages | Social studies | L000License

Attribution-Noncommercial 2.0 UK: England & Wales Attribution-Noncommercial 2.0 UK: England & Wales http://creativecommons.org/licenses/by-nc/2.0/uk/ http://creativecommons.org/licenses/by-nc/2.0/uk/Site sourced from

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See all metadata14.384 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 | MCMCLicense

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.htmSite sourced from

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This class examines tools, data, and ideas related to past climate changes as seen in marine, ice core, and continental records. The most recent climate changes (mainly the past 500,000 years, ranging up to about 2 million years ago) will be emphasized. Quantitative tools for the examination of paleoceanographic data will be introduced (statistics, factor analysis, time series analysis, simple climatology).Subjects

history of the earth-surface environment | deep-sea sediments | ice cores | corals | Micropaleontological | isotopic | geochemical | and mineralogical changes | seawater composition | atmospheric chemistry | climate | ocean temperature | circulation | chemistry | glacial/interglacial cycles | orbital forcing | climate change | marine records | ice core records | continental records | paleoceanographic data | statistics | factor analysis | time series analysis | simple climatology | geochemical changes | mineralogical changes | glacial cycles | intergalacial cycles | earth-surface environment | environmental history | Oxygen Isotope | Coral Reefs | Paleoceanography | Paleoclimatology | Paleothermometry | Atmospheric Carbon Dioxide | Ocean Chemistry | SalinityLicense

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.htmSite sourced from

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See all metadata14.384 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 | MCMCLicense

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.htmSite sourced from

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This class examines tools, data, and ideas related to past climate changes as seen in marine, ice core, and continental records. The most recent climate changes (mainly the past 500,000 years, ranging up to about 2 million years ago) will be emphasized. Quantitative tools for the examination of paleoceanographic data will be introduced (statistics, factor analysis, time series analysis, simple climatology).Subjects

history of the earth-surface environment | deep-sea sediments | ice cores | corals | Micropaleontological | isotopic | geochemical | and mineralogical changes | seawater composition | atmospheric chemistry | climate | ocean temperature | circulation | chemistry | glacial/interglacial cycles | orbital forcing | climate change | marine records | ice core records | continental records | paleoceanographic data | statistics | factor analysis | time series analysis | simple climatology | geochemical changes | mineralogical changes | glacial cycles | intergalacial cycles | earth-surface environment | environmental historyLicense

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.htmSite sourced from

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See all metadata12.864 Inference from Data and Models (MIT)

Description

The course is directed at making scientifically sensible deductions from the combination of observations with dynamics and kinematics represented, generically, as "models". There are two overlapping central themeslinear "inverse" methods and data "assimilation" including regression, singular value decomposition, objective mapping, non-stationary models and data, Kalman filters, adjoint methods ("assimilation") etc.standard time series analysis, including basic statistics, Fourier methods, spectra, coherence, filtering, etc.Subjects

observation | kinematical models | dynamical models | basic statistics | linear algebra | inverse methods | singular value decompositions | control theory | sequential estimation | Kalman filters | smoothing algorithms | adjoint/Pontryagin principle methods | model testing | stationary processes | Fourier methods | z-transforms | sampling theorems | spectra | multi-taper methods | coherences | filtering | quantitative combinations | realistic observations | data assimilations | deduction | regression | objective mapping | time series analysis | inferenceLicense

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.htmSite sourced from

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See all metadata6.435 System Identification (MIT)

Description

This course is offered to graduates and includes topics such as mathematical models of systems from observations of their behavior; time series, state-space, and input-output models; model structures, parametrization, and identifiability; non-parametric methods; prediction error methods for parameter estimation, convergence, consistency, and asymptotic distribution; relations to maximum likelihood estimation; recursive estimation; relation to Kalman filters; structure determination; order estimation; Akaike criterion; bounded but unknown noise model; and robustness and practical issues.Subjects

mathematical models | time series | state-space | input-output models | model structures | parametrization | identifiability | non-parametric methods | prediction error | parameter estimation | convergence | consistency | andasymptotic distribution | maximum likelihood estimation | recursive estimation | Kalman filters | structure determination | order estimation | Akaike criterion | bounded noise models | robustnessLicense

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.htmSite sourced from

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