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Book Chapter: Model Uncertainty, State Uncertainty, and State-space Models

TitleModel Uncertainty, State Uncertainty, and State-space Models
Authors
Issue Date2013
PublisherSpringer
Citation
Model Uncertainty, State Uncertainty, and State-space Models. In Zeng, Y & Wu, S (Eds.), State-Space Models: Applications in Economics and Finance, p. 91-112. New York: Springer, 2013 How to Cite?
AbstractState-space models have been increasingly used to study macroeconomic and financial problems. A state-space representation consists of two equations, a measurement equation which links the observed variables to unobserved state variables and a transition equation describing the dynamics of the state variables. In this chapter, we show that a classic linear-quadratic macroeconomic framework which incorporates two new assumptions can be analytically solved and explicitly mapped to a state-space representation. The two assumptions we consider are the model uncertainty due to concerns for model misspecification (robustness) and the state uncertainty due to limited information constraints (rational inattention). We show that the state-space representation of the observable and unobservable can be used to quantify the key parameters on the degree of model uncertainty. We provide examples on how this framework can be used to study a range of interesting questions in macroeconomics and international economics.
Persistent Identifierhttp://hdl.handle.net/10722/190952
ISBN
Series/Report no.Statistics and Econometrics for Finance; v. 1

 

DC FieldValueLanguage
dc.contributor.authorLuo, Yen_US
dc.contributor.authorNie, Jen_US
dc.contributor.authorYoung, ERen_US
dc.date.accessioned2013-09-17T16:01:27Z-
dc.date.available2013-09-17T16:01:27Z-
dc.date.issued2013en_US
dc.identifier.citationModel Uncertainty, State Uncertainty, and State-space Models. In Zeng, Y & Wu, S (Eds.), State-Space Models: Applications in Economics and Finance, p. 91-112. New York: Springer, 2013en_US
dc.identifier.isbn9781461477884en_US
dc.identifier.urihttp://hdl.handle.net/10722/190952-
dc.description.abstractState-space models have been increasingly used to study macroeconomic and financial problems. A state-space representation consists of two equations, a measurement equation which links the observed variables to unobserved state variables and a transition equation describing the dynamics of the state variables. In this chapter, we show that a classic linear-quadratic macroeconomic framework which incorporates two new assumptions can be analytically solved and explicitly mapped to a state-space representation. The two assumptions we consider are the model uncertainty due to concerns for model misspecification (robustness) and the state uncertainty due to limited information constraints (rational inattention). We show that the state-space representation of the observable and unobservable can be used to quantify the key parameters on the degree of model uncertainty. We provide examples on how this framework can be used to study a range of interesting questions in macroeconomics and international economics.-
dc.languageengen_US
dc.publisherSpringeren_US
dc.relation.ispartofState-Space Models: Applications in Economics and Financeen_US
dc.relation.ispartofseriesStatistics and Econometrics for Finance; v. 1-
dc.titleModel Uncertainty, State Uncertainty, and State-space Modelsen_US
dc.typeBook_Chapteren_US
dc.identifier.emailLuo, Y: yluo@econ.hku.hken_US
dc.identifier.authorityLuo, Y=rp01083en_US
dc.identifier.doi10.1007/978-1-4614-7789-1_4-
dc.identifier.hkuros224075en_US
dc.identifier.spage91en_US
dc.identifier.epage112en_US
dc.publisher.placeNew Yorken_US

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