Overview
- Authors:
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Jati K. Sengupta
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University of California, Santa Barbara, USA
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Phillip Fanchon
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California Polytechnic, San Luis Obispo, USA
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Table of contents (7 chapters)
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- Jati K. Sengupta, Phillip Fanchon
Pages 1-8
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- Jati K. Sengupta, Phillip Fanchon
Pages 9-62
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- Jati K. Sengupta, Phillip Fanchon
Pages 63-96
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- Jati K. Sengupta, Phillip Fanchon
Pages 97-145
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- Jati K. Sengupta, Phillip Fanchon
Pages 147-180
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- Jati K. Sengupta, Phillip Fanchon
Pages 181-210
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- Jati K. Sengupta, Phillip Fanchon
Pages 211-248
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Back Matter
Pages 249-258
About this book
Control theory methods in economics have historically developed over three phases. The first involved basically the feedback control rules in a deterministic framework which were applied in macrodynamic models for analyzing stabilization policies. The second phase raised the issues of various types of inconsistencies in deterministic optimal control models due to changing information and other aspects of stochasticity. Rational expectations models have been extensively used in this plan to resolve some of the inconsistency problems. The third phase has recently focused on the various aspects of adaptive control. where stochasticity and information adaptivity are introduced in diverse ways e.g .• risk adjustment and risk sensitivity of optimal control, recursive updating rules via Kalman filtering and weighted recursive least squares and variable structure control methods in nonlinear framework. Problems of efficient econometric estimation of optimal control models have now acquired significant importance. This monograph provides an integrated view of control theory methods, synthesizing the three phases from feedback control to stochastic control and from stochastic control to adaptive control. Aspects of econometric estimation are strongly emphasized here, since these are very important in empirical applications in economics.
Authors and Affiliations
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University of California, Santa Barbara, USA
Jati K. Sengupta
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California Polytechnic, San Luis Obispo, USA
Phillip Fanchon
