This is a project to develop a document for teaching graduate econometrics that is "open source", specifically, licensed as GNU GPL. That is, anyone can access the document in editable form, and can modify it, as long as they make their modifications available. This allows for personalization, as well as a simple way to make contributions and error corrections. The hope is that people preparing to teach econometrics for the first time might find it useful, and eventually be motivated to contribute back to the project. The central document is something between a set of lecture notes and a text book. It's not as terse as lecture notes, but not as complete or well-referenced as a text book. Of course, the document is constantly evolving, and you are welcome to modify it as you like. The document contains (at least when viewed in HTML or PDF form) hyperlinks to example programs written using the GNU/Octave language. The document itself is written using the LyX word processor. LyX documents can be exported as LaTeX, so the system is quite portable.

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. Upon successful completion of this course, students will be able to: Explain the fundamental probability concepts used in econometric analysis; Discuss the issues and pitfalls involved in testing theories; Demonstrate an understanding of the formulation of an empirical economic model; Perform data collection, interpretation, organization, and analysis for economics; Identify the desirable properties of estimators; Identify key classical assumptions in the field of Econometrics, explain their significance, and describe the effects that violations of the classical assumptions can have; Demonstrate an understanding of the basics of econometric analysis focusing on the least squares methodology for single explanatory and multiple explanatory variables; Extend to the regression 'family' to handle important special cases; Interpret key statistics and diagnostics typically generated by software. (Economics 203; See also: Mathematics 301)

Specification and estimation of the linear regression model. Departures from the standard Gauss-Markov assumptions 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.

Introduction to econometric models and techniques, emphasizing regression. Advanced topics include instrumental variables, panel data methods, measurement error, and limited dependent variable models. Includes problem sets. May not count toward HASS requirement.

Econometrics is the study of estimation and inference for economic models using economic data. Econometric theory concerns the study and development of tools and methods for applied econometric applications. Applied econometrics concerns the application of these tools to economic data.

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.

Neoclassical analysis of the labor market and its institutions. A systematic development of the theory of labor supply, labor demand, and human capital theory. Topics discussed also include theories of wage and employment determination, turnover, search, unemployment, equalizing differences, and union behavior. Particular emphasis on the interaction of 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. 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. 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.

Focuses on recent developments in econometrics. Topics include empirical processes and asymptotic theory, nonparametric and semiparametric estimation, estimation of auction and other structural models, unit roots and cointegration, and continuous time econometrics. Results illustrated with economic applications.

Mathematical economics involves the application of mathematics to the theoretical aspects of economic analysis while econometrics deals with the study of empirical observations using statistical methods of estimation and hypothesis testing. These two are complementary and theories must be tested against empirical data for validity, and statistical work needs economic theory as a guide in order to determine the appropriate direction of research. This resource contains structured tutorials for Introductory Mathematics for Economics.

Focus on multivariate data analysis procedures, emphasizing regression. Considers model specification, autocorrelation, instrumental variables, and causal modelling. Students must have taken at least one previous subject in statistics. Open to qualified undergraduates. Course home page description: This course is the second semester in the statistics sequence for political science and public policy offered in the Political Science Department at MIT. The intellectual thrust of the course is a presentation of statistical models for estimating causal effects of variables. The model of an effect is a conditional mean (though we might imagine other effect). The notion of causality is the effect of one variable on another holding all else constant.

The course introduces statistical theory to prepare 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, building functional forms, regression algebra, Gauss-Markov optimality, finite-sample inference, consistency, asymptotic normality, heteroscedasticity, and autocorrelation.

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.