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Recursive robust estimation and control without commitment

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dc.creator Hansen, Lars Peter
dc.creator Sargent, Thomas J.
dc.date 2005
dc.date.accessioned 2013-10-16T07:05:54Z
dc.date.available 2013-10-16T07:05:54Z
dc.date.issued 2013-10-16
dc.identifier http://hdl.handle.net/10419/19613
dc.identifier ppn:500567581
dc.identifier RePEc:zbw:bubdp1:4222
dc.identifier.uri http://koha.mediu.edu.my:8181/xmlui/handle/10419/19613
dc.description In a Markov decision problem with hidden state variables, a posterior distribution serves as a state variable and Bayes' law under an approximating model gives its law of motion. A decision maker expresses fear that his model is misspecified by surrounding it with a set of alternatives that are nearby when measured by their expected log likelihood ratios (entropies). Martingales represent alternative models. A decision maker constructs a sequence of robust decision rules by pretending that a sequence of minimizing players choose increments to a martingale and distortions to the prior over the hidden state. A risk sensitivity operator induces robustness to perturbations of the approximating model conditioned on the hidden state. Another risk sensitivity operator induces robustness to the prior distribution over the hidden state. We use these operators to extend the approach of Hansen and Sargent (1995) to problems that contain hidden states. The worst case martingale is overdetermined, expressing an intertemporal inconsistency of worst case beliefs about the hidden state, but not about observables.
dc.language eng
dc.relation Discussion paper Series 1 / Volkswirtschaftliches Forschungszentrum der Deutschen Bundesbank 2005,28
dc.rights http://www.econstor.eu/dspace/Nutzungsbedingungen
dc.subject ddc:330
dc.subject Entscheidung bei Unsicherheit
dc.subject Unvollkommene Information
dc.subject Martingale
dc.subject Robustes Verfahren
dc.subject Geldpolitik
dc.subject Theorie
dc.title Recursive robust estimation and control without commitment
dc.type doc-type:workingPaper


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