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Learning in Economic Systems with Expectations Feedback

By: Wenzelburger, Jan [author.].
Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Lecture Notes in Economics and Mathematical Systems: 555Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2006.Description: X, 176 p. 4 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783540380504.Subject(s): Economic theory | Econometrics | Macroeconomics | Economics | Economic Theory/Quantitative Economics/Mathematical Methods | Econometrics | Macroeconomics/Monetary Economics//Financial EconomicsDDC classification: 330.1 Online resources: Click here to access online
Contents:
Economic Systems With Expectations Feedback -- Adaptive Learning in Linear Models -- Economic Models Subject to Stationary Noise -- Nonparametric Adaptive Learning -- Stochastic Exchange Economies -- Heterogeneous Beliefs in a Financial Market.
In: Springer eBooksSummary: Recently economists have more and more focussed on scenarios in which agents' views of the world may be erroneous. These notes introduce the concept of perfect forecasting rules which provide best least-squares predictions along the evolution of an economic system. The framework for nonparametric adaptive learning schemes is developed and it is argued that plausible learning schemes should aim at estimating a perfect forecasting rule taking into account the correct feedback structure of an economy. A link is provided between the traditional rational-expectations view and recent behavioristic approaches.
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E books E books PK Kelkar Library, IIT Kanpur
Available EBK5733
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Economic Systems With Expectations Feedback -- Adaptive Learning in Linear Models -- Economic Models Subject to Stationary Noise -- Nonparametric Adaptive Learning -- Stochastic Exchange Economies -- Heterogeneous Beliefs in a Financial Market.

Recently economists have more and more focussed on scenarios in which agents' views of the world may be erroneous. These notes introduce the concept of perfect forecasting rules which provide best least-squares predictions along the evolution of an economic system. The framework for nonparametric adaptive learning schemes is developed and it is argued that plausible learning schemes should aim at estimating a perfect forecasting rule taking into account the correct feedback structure of an economy. A link is provided between the traditional rational-expectations view and recent behavioristic approaches.

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