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An introduction to probability and statistical methods for empirical work in economics. Probability, random variables, sampling, descriptive statistics, probability distributions, estimation and hypothesis testing, introduction to the regression model. Economic data sources, economic applications, and the use of statistical software packages will be emphasized. Two 90-minute classes, one preceptorial. Prerequisites: 100 and 101, MAT 103r
The objective of this course is to prepare students for basic empirical work in economics. In particular, topics will include basic data analysis, regression analysis, testing, and forecasting. Students will be provided with the opportunity to use actual economic data to test economic theories. Prerequisites: 100 or 101, and 202; MAT 103. Two 90-minute classes, one preceptorial.
Statistical analysis of economic data. The two-variable regression model, multiple regression. Techniques for dealing with violations of the regression model’s assumptions, including autocorrelation, heteroscedasticity, specification error, and measurement error. Dummy variables, discrete-choice models, time series models, and forecasting. Introduction to simultaneous equations. Estimation and testing of economic models will be an important part of the course. Prerequisites: 100 and 101 and 202 (or ORF 245); MAT 200 or 201 or equivalent. Two 90-minute lectures, one class.
This course provides hands-on experience in econometric analysis designed to help students to acquire the skills necessary to carry out their own empirical research in economics. Various aspects of empirical research in economics will be covered, including development of testable economic models, appropriate use of data, and specification and estimation of econometric models. Prerequisites: 302 or 312; and calculus. Two lectures, one preceptorial.
Concepts and methods of time series analysis and their applications to economics. Time series models to be studied include simultaneous stochastic equations and VAR, ARIMA, and state-space models. Methods to analyze trends, second-moment properties via the auto covariance function and the spectral density function, and methods of estimation and hypothesis testing and of model selection are presented. Kalman filter and applications as well as unit roots, cointegration, ARCH, and structural breaks models are also studied.
The construction, estimation, and testing of econometric models as a process, from theory to model formulation to estimation and testing, and back again to theory. Bridging the gap between theory and applied work. A series of topics in macroeconomic time series and microeconomic cross-sectional analysis that include consumption at the household and aggregate level, commodity prices, and nonparametric and parametric estimation.
A first-year course in the econometrics sequence; it is divided into two parts. The first gives students the necessary background in probability theory and statistics. Topics include definitions and axioms of probability, moments, some univariate distributions, the multivariate normal distribution, sampling distributions, introduction to asymptotic theory, estimation, and testing. The second introduces the linear regression model, and develops associated tools. Properties of the ordinary least-squares estimator are studied in detail, and a number of tests developed.
This course begins with extensions of the linear model in several directions: (1) predetermined but not exogenous regressors; (2) heteroskedasticity and serial correlation; (3) classical GLS; (4) instrumental variables and generalized method of moments estimators. Applications include simultaneous equation models, VAR’s, and panel data. Estimation and inference in nonlinear models are discussed. Applications include nonlinear least squares, discrete dependent variables (probit, logit, etc.), problems of censoring, truncation and sample selection, and models for duration data.
ECO 519 is half of the second-year sequence in econometrics methodology (ECO 513 is the other). The course covers nonlinear statistical models for the analysis of cross-sectional and panel data. It is intended both for students specializing in econometric theory, and for students interested in applying statistical methods to statistical data. Approximately half of the course is devoted to development of the large-sample theory for nonlinear estimation procedures, while the other half concentrates on application of the methods to econometric models for discrete and limited dependent variables.
Drafts of papers, articles, and chapters of dissertations or books, prepared by graduate students, faculty members, or visiting scholars, are exposed to critical analysis by a series of seminars organized by field. The chief objectives are for the writers to receive the benefit of critical suggestions, for all participants to gain experience in criticism and uninhibited oral discussion, and for students and faculty members to become acquainted with the research work going on in the department. Third- and fourth-year graduate students are expected to attend; first-and second-year students and faculty members are invited to attend.