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For example, marathon OR race. • • • • • • • A list of topics by session is given in the below. Course Meeting Times Lectures: 2 sessions / week, 1.5 hours / session Recitations: 1 session / week, 1.5 hours / session Description This course focuses on dynamic optimization methods, both in discrete and in continuous time. We approach these problems from a dynamic programming and optimal control perspective.

We also study the dynamic systems that come from the solutions to these problems. The course will illustrate how these techniques are useful in various applications, drawing on many economic examples. However, the focus will remain on gaining a general command of the tools so that they can be applied later in other classes. Prerequisites The prerequisites for this course are 14.06 Advanced Macroeconomics or permission of the instructor.

Textbooks Required Stokey, Nancy L., and Robert E. Lucas, Jr., with Edward C. Recursive Methods in Economic Dynamics. Cambridge, MA: Harvard University Press, 1989. Acemoglu, Daron.

Princeton, NJ: Princeton University Press, 2008. Recommended Chiang, Alpha C.

Elements of Dynamic Optimization. Long Grove, IL: Waveland Press, 1999. Luenberger, David. Optimization by Vector Space Methods.

New York, NY: Wiley-Interscience, 1997. First published in 1969 by John Wiley and Sons, Inc. Kamien, Morton I., and Nancy L. Dynamic Optimization: The Calculus of Variations and Optimal Control in Economics and Management. San Diego, CA: Elsevier, 1991. Course Requirements There will be several problem sets and a final exam. The problem sets will cover numerical methods related to lecture material.

This is one of over 2,200 courses on OCW. Find materials for this course in the pages linked along the left. Uchebnik po farmakognozii zhohova. MIT OpenCourseWare is a free & open publication of material from thousands of MIT courses, covering the entire MIT curriculum. No enrollment or registration. Freely browse and use OCW materials at your own pace. There's no signup, and no start or end dates. Knowledge is your reward.