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14.661 Labor Economics I (MIT) 14.661 Labor Economics I (MIT)

Description

Neoclassical analysis of the labor market and its institutions. A systematic development of the theory of labor supply, labor demand, and human capital. Topics discussed also include wage and employment determination, turnover, search, immigration, unemployment, equalizing differences, and institutions in the labor market. There is particular emphasis on the interaction of theoretical and empirical modeling and the development of independent research interests. Neoclassical analysis of the labor market and its institutions. A systematic development of the theory of labor supply, labor demand, and human capital. Topics discussed also include wage and employment determination, turnover, search, immigration, unemployment, equalizing differences, and institutions in the labor market. There is particular emphasis on the interaction of theoretical and empirical modeling and the development of independent research interests.Subjects

labor economics | public policy | schooling | learning | matching | experience | wages | minimum wage | college | investment | training | firms | corporations | labor | unions | panel data | neoclassical model | turnover models | turnover | economics | labor economics | public policy | schooling | learning | matching | experience | wages | minimum wage | college | investment | training | firms | corporations | labor | unions | panel data | neoclassical model | turnover models | turnover | economics | labor | labor | market | market | statistics | statistics | theory | theory | neoclassical | neoclassical | supply | supply | model | model | life-cycle | life-cycle | demand | demand | wages | wages | immigration | immigration | human capital | human capital | econometrics | econometrics | liquidity | liquidity | constraints | constraints | mobility | mobility | incentives | incentives | organization | organization | moral hazard | moral hazard | insurance | insurance | investments | investments | efficiency | efficiency | unemployment | unemployment | search | search | jobs | jobs | training | training | capital | capital | firm | firm | technology | technology | skills | skills | risk | risk | signaling | signaling | discrimination | discrimination | self-selection | self-selection | learning | learning | natives | nativesLicense

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 metadata14.33 Economics Research and Communication (MIT) 14.33 Economics Research and Communication (MIT)

Description

This course will guide students through the process of forming economic hypotheses, gathering the appropriate data, analyzing them, and effectively communicating their results. This course will guide students through the process of forming economic hypotheses, gathering the appropriate data, analyzing them, and effectively communicating their results.Subjects

Economics | Economics | research | research | communication | communication | hypotheses | hypotheses | data | data | analysis | analysis | results | results | STATA | STATA | data sets | data sets | writing | writing | econometrics | econometricsLicense

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See all metadata14.661 Labor Economics I (MIT) 14.661 Labor Economics I (MIT)

Description

The aim of this course is to acquaint students with traditional topics in labor economics and to encourage the development of independent research interests. We will cover a systematic development of the theory of labor supply, labor demand, and human capital. Topics include wage and employment determination, turnover, search, immigration, unemployment, equalizing differences, and institutions in the labor market. There will be particular emphasis on the interaction between theoretical and empirical modeling. The aim of this course is to acquaint students with traditional topics in labor economics and to encourage the development of independent research interests. We will cover a systematic development of the theory of labor supply, labor demand, and human capital. Topics include wage and employment determination, turnover, search, immigration, unemployment, equalizing differences, and institutions in the labor market. There will be particular emphasis on the interaction between theoretical and empirical modeling.Subjects

labor economics | labor economics | public policy | public policy | immigration | immigration | human capital | human capital | econometrics | econometrics | minimum wage | minimum wage | public education | public education | job training | job training | labor | labor | unions | unions | neoclassical model | neoclassical model | life-cycle | life-cycle | insurance | insurance | unemployment | unemployment | signaling | signalingLicense

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 covers empirical strategies for applied micro research questions. Our agenda includes regression and matching, instrumental variables, differences-in-differences, regression discontinuity designs, standard errors, and a module consisting of 8–9 lectures on the analysis of high-dimensional data sets a.k.a. "Big Data". This course covers empirical strategies for applied micro research questions. Our agenda includes regression and matching, instrumental variables, differences-in-differences, regression discontinuity designs, standard errors, and a module consisting of 8–9 lectures on the analysis of high-dimensional data sets a.k.a. "Big Data".Subjects

econometrics | econometrics | big data | big data | research | research | economics | economics | regression | regression | matching | matching | instrumental variables | instrumental variables | differences-in-differences | differences-in-differences | standard errors | standard errors | high-dimensional data sets | high-dimensional data setsLicense

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 will provide a solid foundation in probability and statistics for economists and other social scientists. We will emphasize topics needed for further study of econometrics and provide basic preparation for 14.32. Topics include elements of probability theory, sampling theory, statistical estimation, and hypothesis testing. This course will provide a solid foundation in probability and statistics for economists and other social scientists. We will emphasize topics needed for further study of econometrics and provide basic preparation for 14.32. Topics include elements of probability theory, sampling theory, statistical estimation, and hypothesis testing.Subjects

statistics | statistics | economic applications | economic applications | probability theory | probability theory | sampling theory | sampling theory | statistical estimation | statistical estimation | regression analysis | regression analysis | hypothesis testing | hypothesis testing | Elementary econometrics | Elementary econometrics | statistical tools | statistical tools | economic data | economic data | economics | economics | statistical | statistical | probability distribution function | probability distribution function | cumulative distribution function | cumulative distribution function | normal | normal | Student's t | Student's t | chi-squared | chi-squared | central limit theorem | central limit theorem | law of large numbers | law of large numbers | Bayes theorem | Bayes theoremLicense

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 metadata14.386 New Econometric Methods (MIT) 14.386 New Econometric Methods (MIT)

Description

This course focuses on recent developments in econometrics, especially structural estimation. The topics include nonseparable models, models of imperfect competition, auction models, duration models, and nonlinear panel data. Results are illustrated with economic applications. This course focuses on recent developments in econometrics, especially structural estimation. The topics include nonseparable models, models of imperfect competition, auction models, duration models, and nonlinear panel data. Results are illustrated with economic applications.Subjects

econometrics | econometrics | recent developments | recent developments | structural estimation | structural estimation | nonseparable models | nonseparable models | models of imperfect competition | models of imperfect competition | auction models | auction models | duration models | duration models | and nonlinear panel data | and nonlinear panel data | economic applications | economic applicationsLicense

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 metadata14.381 Statistical Method in Economics (MIT) 14.381 Statistical Method in Economics (MIT)

Description

This course is divided into two sections, Part I and Part II. Part I provides an introduction to statistical theory and can be found by visiting 14.381 Fall 2013. Part II, found here, prepares students for the remainder of the econometrics sequence. The emphasis of the course is to understand the basic principles of statistical theory. A brief review of probability will be given; however, this material is assumed knowledge. The course also covers basic regression analysis. Topics covered include probability, random samples, asymptotic methods, point estimation, evaluation of estimators, Cramer-Rao theorem, hypothesis tests, Neyman Pearson lemma, Likelihood Ratio test, interval estimation, best linear predictor, best linear approximation, conditional expectation function, buil This course is divided into two sections, Part I and Part II. Part I provides an introduction to statistical theory and can be found by visiting 14.381 Fall 2013. Part II, found here, prepares students for the remainder of the econometrics sequence. The emphasis of the course is to understand the basic principles of statistical theory. A brief review of probability will be given; however, this material is assumed knowledge. The course also covers basic regression analysis. Topics covered include probability, random samples, asymptotic methods, point estimation, evaluation of estimators, Cramer-Rao theorem, hypothesis tests, Neyman Pearson lemma, Likelihood Ratio test, interval estimation, best linear predictor, best linear approximation, conditional expectation function, builSubjects

statistical theory | statistical theory | econometrics | econometrics | regression analysis | regression analysis | probability | probability | random samples | random samples | asymptotic methods | asymptotic methods | point estimation | point estimation | evaluation of estimators | evaluation of estimators | Cramer-Rao theorem | Cramer-Rao theorem | hypothesis tests | hypothesis tests | Neyman Pearson lemma | Neyman Pearson lemma | Likelihood Ratio test | Likelihood Ratio test | interval estimation | interval estimation | best linear predictor | best linear predictor | best linear approximation | best linear approximation | conditional expectation function | conditional expectation function | building functional forms | building functional forms | regression algebra | regression algebra | Gauss-Markov optimality | Gauss-Markov optimality | finite-sample inference | finite-sample inference | consistency | consistency | asymptotic normality | asymptotic normality | heteroscedasticity | heteroscedasticity | autocorrelation | autocorrelationLicense

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See all metadata14.32 Econometrics (MIT) 14.32 Econometrics (MIT)

Description

Introduction to econometric models and techniques, simultaneous equations, program evaluation, emphasizing regression. Advanced topics include instrumental variables, panel data methods, measurement error, and limited dependent variable models. May not count toward HASS requirement. Introduction to econometric models and techniques, simultaneous equations, program evaluation, emphasizing regression. Advanced topics include instrumental variables, panel data methods, measurement error, and limited dependent variable models. May not count toward HASS requirement.Subjects

econometrics | econometrics | statistical methods | statistical methods | differences-in-differences | differences-in-differences | 2SLS | 2SLS | FGLS | FGLS | serial correlation | serial correlation | IV | IV | two-stage least squares | two-stage least squares | multivariate regression | multivariate regression | simultaneous equations | simultaneous equations | econometric models | econometric models | program evaluation | program evaluation | linear regression | linear regression | instrumental variables | instrumental variables | panel data methods | panel data methods | measurement error | measurement error | limited dependent variable models | limited dependent variable modelsLicense

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 self-contained introduction to statistics with economic applications. Elements of probability theory, sampling theory, statistical estimation, regression analysis, and hypothesis testing. It uses elementary econometrics and other applications of statistical tools to economic data. It also provides a solid foundation in probability and statistics for economists and other social scientists. We will emphasize topics needed in the further study of econometrics and provide basic preparation for 14.32. No prior preparation in probability and statistics is required, but familiarity with basic algebra and calculus is assumed. This course is a self-contained introduction to statistics with economic applications. Elements of probability theory, sampling theory, statistical estimation, regression analysis, and hypothesis testing. It uses elementary econometrics and other applications of statistical tools to economic data. It also provides a solid foundation in probability and statistics for economists and other social scientists. We will emphasize topics needed in the further study of econometrics and provide basic preparation for 14.32. No prior preparation in probability and statistics is required, but familiarity with basic algebra and calculus is assumed.Subjects

statistics | statistics | economic applications | economic applications | probability theory | probability theory | sampling theory | sampling theory | statistical estimation | statistical estimation | regression analysis | regression analysis | hypothesis testing | hypothesis testing | Elementary econometrics | Elementary econometrics | statistical tools | statistical tools | economic data | economic data | economics | economics | statistical | statisticalLicense

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 metadata14.382 Econometrics I (MIT) 14.382 Econometrics I (MIT)

Description

This course focuses on the specification and estimation of the linear regression model. The course departs from the standard Gauss-Markov assumptions to include heteroskedasticity, serial correlation, and errors in variables. Advanced topics include generalized least squares, instrumental variables, nonlinear regression, and limited dependent variable models. Economic applications are discussed throughout the course. This course focuses on the specification and estimation of the linear regression model. The course departs from the standard Gauss-Markov assumptions to include heteroskedasticity, serial correlation, and errors in variables. Advanced topics include generalized least squares, instrumental variables, nonlinear regression, and limited dependent variable models. Economic applications are discussed throughout the course.Subjects

Economics | Economics | econometrics | econometrics | linear regression model | linear regression model | Gauss-Markov | Gauss-Markov | heteroskedasticity | heteroskedasticity | serial correlation | serial correlation | errors | errors | variables | variables | generalized least squares | generalized least squares | instrumental variables | instrumental variables | nonlinear regression | nonlinear regression | limited dependent variable models | limited dependent variable modelsLicense

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 metadata14.661 Labor Economics I (MIT) 14.661 Labor Economics I (MIT)

Description

The aim of this course is to acquaint students with traditional topics in labor economics and to encourage the development of independent research interests. This course is taught in two parts: Fall term and then in the subsequent Fall term. The aim of this course is to acquaint students with traditional topics in labor economics and to encourage the development of independent research interests. This course is taught in two parts: Fall term and then in the subsequent Fall term.Subjects

Economics | Economics | labor | labor | market | market | statistics | statistics | theory | theory | neoclassical | neoclassical | supply | supply | model | model | life-cycle | life-cycle | demand | demand | wages | wages | immigration | immigration | human capital | human capital | econometrics | econometrics | liquidity | liquidity | constraints | constraints | mobility | mobility | incentives | incentives | organization | organization | moral hazard | moral hazard | insurance | insurance | investments | investments | efficiency | efficiency | unemployment | unemployment | search | search | jobs | jobs | training | training | capital | capital | firm | firm | technology | technology | skills | skills | risk | risk | signaling | signaling | discrimination | discrimination | self-selection | self-selection | learning | learning | natives | nativesLicense

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 self-contained introduction to statistics with economic applications. Elements of probability theory, sampling theory, statistical estimation, regression analysis, and hypothesis testing. It uses elementary econometrics and other applications of statistical tools to economic data. It also provides a solid foundation in probability and statistics for economists and other social scientists. We will emphasize topics needed in the further study of econometrics and provide basic preparation for 14.32. No prior preparation in probability and statistics is required, but familiarity with basic algebra and calculus is assumed. This course is a self-contained introduction to statistics with economic applications. Elements of probability theory, sampling theory, statistical estimation, regression analysis, and hypothesis testing. It uses elementary econometrics and other applications of statistical tools to economic data. It also provides a solid foundation in probability and statistics for economists and other social scientists. We will emphasize topics needed in the further study of econometrics and provide basic preparation for 14.32. No prior preparation in probability and statistics is required, but familiarity with basic algebra and calculus is assumed.Subjects

statistics | statistics | economic applications | economic applications | probability theory | probability theory | sampling theory | sampling theory | statistical estimation | statistical estimation | regression analysis | regression analysis | hypothesis testing | hypothesis testing | Elementary econometrics | Elementary econometrics | statistical tools | statistical tools | economic data | economic data | economics | economics | statistical | statisticalLicense

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 metadata15.450 Analytics of Finance (MIT) 15.450 Analytics of Finance (MIT)

Description

This course covers the key quantitative methods of finance: financial econometrics and statistical inference for financial applications; dynamic optimization; Monte Carlo simulation; stochastic (Itô) calculus. These techniques, along with their computer implementation, are covered in depth. Application areas include portfolio management, risk management, derivatives, and proprietary trading. This course covers the key quantitative methods of finance: financial econometrics and statistical inference for financial applications; dynamic optimization; Monte Carlo simulation; stochastic (Itô) calculus. These techniques, along with their computer implementation, are covered in depth. Application areas include portfolio management, risk management, derivatives, and proprietary trading.Subjects

financial econometrics | financial econometrics | statistical inference | statistical inference | dynamic optimization | dynamic optimization | Monte Carlo simulation | Monte Carlo simulation | stochastic (Itô) calculus | stochastic (Itô) calculus | portfolio management | portfolio management | risk management | risk management | proprietary trading | proprietary trading | derivative pricing | derivative pricing | generalized method of moments | generalized method of moments | Black-Scholes model | Black-Scholes 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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See all metadata14.33 Economics Research and Communication (MIT) 14.33 Economics Research and Communication (MIT)

Description

This course will guide students through the process of forming economic hypotheses, gathering the appropriate data, analyzing them, and effectively communicating their results. All students will be expected to have successfully completed Introduction to Statistical Methods in Economics and Econometrics (or their equivalents) as well as courses in basic microeconomics and macroeconomics. Students may find it useful to take at least one economics field course and perform a UROP before taking this course, but these are not requirements. This course will guide students through the process of forming economic hypotheses, gathering the appropriate data, analyzing them, and effectively communicating their results. All students will be expected to have successfully completed Introduction to Statistical Methods in Economics and Econometrics (or their equivalents) as well as courses in basic microeconomics and macroeconomics. Students may find it useful to take at least one economics field course and perform a UROP before taking this course, but these are not requirements.Subjects

empirical economics | empirical economics | econometrics | econometrics | mathematical economics | mathematical economics | statistics | statisticsLicense

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 metadata14.33 Economics Research and Communication (MIT) 14.33 Economics Research and Communication (MIT)

Description

This course will guide students through the process of forming economic hypotheses, gathering the appropriate data, analyzing them, and effectively communicating their results. All students will be expected to have successfully completed Introduction to Statistical Methods in Economics and Econometrics (or their equivalents) as well as courses in basic microeconomics and macroeconomics. Students may find it useful to take at least one economics field course and perform a UROP before taking this course, but these are not requirements. This course will guide students through the process of forming economic hypotheses, gathering the appropriate data, analyzing them, and effectively communicating their results. All students will be expected to have successfully completed Introduction to Statistical Methods in Economics and Econometrics (or their equivalents) as well as courses in basic microeconomics and macroeconomics. Students may find it useful to take at least one economics field course and perform a UROP before taking this course, but these are not requirements.Subjects

empirical economics | empirical economics | econometrics | econometrics | mathematical economics | mathematical economics | statistics | statistics | Economics | Economics | research | research | communication | communication | hypotheses | hypotheses | data | data | analysis | analysis | results | results | STATA | STATA | data sets | data sets | writing | writingLicense

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 will provide a solid foundation in probability and statistics for economists and other social scientists. We will emphasize topics needed in the further study of econometrics and provide basic preparation for 14.32. No prior preparation in probability and statistics is required, but familiarity with basic algebra and calculus is assumed. This course will provide a solid foundation in probability and statistics for economists and other social scientists. We will emphasize topics needed in the further study of econometrics and provide basic preparation for 14.32. No prior preparation in probability and statistics is required, but familiarity with basic algebra and calculus is assumed.Subjects

Economics | Economics | statistics | statistics | methods | methods | probability | probability | economists | economists | social scientists | social scientists | econometrics | econometrics | algebra | algebra | calculus | calculusLicense

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 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 metadataWhat is econometrics for? - Introductory Handout

Description

This short handout contributes to the motivation/introduction of an econometrics module. It takes students through the key features of a published example of applied econometrics.Subjects

economics | econometrics | ukoer | trueproject | applied econometrics | 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 metadata14.30 Introduction to Statistical Methods in Economics (MIT)

Description

This course will provide a solid foundation in probability and statistics for economists and other social scientists. We will emphasize topics needed for further study of econometrics and provide basic preparation for 14.32. Topics include elements of probability theory, sampling theory, statistical estimation, and hypothesis testing.Subjects

statistics | economic applications | probability theory | sampling theory | statistical estimation | regression analysis | hypothesis testing | Elementary econometrics | statistical tools | economic data | economics | statistical | probability distribution function | cumulative distribution function | normal | Student's t | chi-squared | central limit theorem | law of large numbers | Bayes theoremLicense

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 course is designed to provide the student with a simple and straightforward introduction to econometrics. Econometrics is a set of research tools employed in the business disciplines of accounting, finance, marketing, and management. It is also used by social scientists, specifically researchers in history, political science, and sociology, and it even plays an important role in such diverse fields as forestry and agricultural economics. Studying econometrics will help the student transition from being a student of economics to a practicing economist. By taking this course, the student will gain an overview of what econometrics is about and develop some “intuition” about how things work. This free course may be completed online at any time. See course site for detailed overvieSubjects

probability | statistics | econometrics | linear regression | multiple regression | transformations | dummy variables | heteroscedasticity | stochastic regressors | Social studies | L000License

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See all metadata14.30 Introduction to Statistical Method in Economics (MIT)

Description

This course will provide a solid foundation in probability and statistics for economists and other social scientists. We will emphasize topics needed in the further study of econometrics and provide basic preparation for 14.32. No prior preparation in probability and statistics is required, but familiarity with basic algebra and calculus is assumed.Subjects

Economics | statistics | methods | probability | economists | social scientists | econometrics | algebra | calculusLicense

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 metadata14.386 New Econometric Methods (MIT)

Description

This course focuses on recent developments in econometrics, especially structural estimation. The topics include nonseparable models, models of imperfect competition, auction models, duration models, and nonlinear panel data. Results are illustrated with economic applications.Subjects

econometrics | recent developments | structural estimation | nonseparable models | models of imperfect competition | auction models | duration models | and nonlinear panel data | economic applicationsLicense

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Description

?This course is divided into two sections, Part I and Part II. Part I provides an introduction to statistical theory and can be found by visiting 14.381 Fall 2013. Part II, found here, prepares students for the remainder of the econometrics sequence. The emphasis of the course is to understand the basic principles of statistical theory. A brief review of probability will be given; however, this material is assumed knowledge. The course also covers basic regression analysis. Topics covered include probability, random samples, asymptotic methods, point estimation, evaluation of estimators, Cramer-Rao theorem, hypothesis tests, Neyman Pearson lemma, Likelihood Ratio test, interval estimation, best linear predictor, best linear approximation, conditional expectation function, builSubjects

statistical theory | econometrics | regression analysis | probability | random samples | asymptotic methods | point estimation | evaluation of estimators | Cramer-Rao theorem | hypothesis tests | Neyman Pearson lemma | Likelihood Ratio test | interval estimation | best linear predictor | best linear approximation | conditional expectation function | building functional forms | regression algebra | Gauss-Markov optimality | finite-sample inference | consistency | asymptotic normality | heteroscedasticity | autocorrelationLicense

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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Introduction to econometric models and techniques, simultaneous equations, program evaluation, emphasizing regression. Advanced topics include instrumental variables, panel data methods, measurement error, and limited dependent variable models. May not count toward HASS requirement.Subjects

econometrics | statistical methods | differences-in-differences | 2SLS | FGLS | serial correlation | IV | two-stage least squares | multivariate regression | simultaneous equations | econometric models | program evaluation | linear regression | instrumental variables | panel data methods | measurement error | limited dependent variable modelsLicense

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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