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The Economics Ph.D. core curriculum consists of core courses in microtheory (EC 740, 741), macrotheory (EC 750, 751), mathematics for economists (EC 720), statistics (EC 770) and econometrics (EC 771). Course descriptions of the core courses are presented below.
This course consists of two modules: one on linear algebra and the other on economic dynamics. The linear algebra portion of the course covers fundamental material in vector spaces, metric spaces, linear equations and matrices, determinants, and linear algebra. This basic material finds application in numerous economics courses, including macro theory, micro theory, and econometrics, and will be assumed in the theoretical econometrics sequence. The economic dynamics portion of the course covers differential equations, difference equations, and various topics in dynamic optimization.
This course covers basic consumer and producer theory and expected utility maximization. Also covered are special topics in consumer theory such as welfare change measures and revealed preference theory.
This course comprises three modules. The first treats pure and applied aspects of general equilibrium theory. The second is an introduction to non-cooperative game theory. The third covers topics in information economics.
The first half of the course presents Keynesian and classical models, rational expectations and its implications for aggregate supply, and economic policy. The second half covers the Solow growth model, infinite horizon and overlapping generation models, the new growth theory, real business cycle theory, and traditional Keynesian theories of fluctuations.
Microeconomic foundations of nominal rigidities, real rigidities and the labor market, consumption and investment under uncertainty, theories of asset prices, the demand for money and the effect of monetary policy and dynamic consistency and economic policy.
The first part of this course deals with topics in probability theory, including random variables, expectation, conditional distributions, and limit theorems. The second part covers topics in statistics, including maximum likelihood estimation, method of moments, hypothesis testing, and large sample inference.
This is a first year graduate course in econometrics. Topics include estimation and inference in classical regression analysis, estimation by maximum likelihood, generalized methods of moments, simultaneous equation models, time series models, and panel data methods.
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http://www.bc.edu/economics |