Arbeitspapier
Nonparametric identification of positive eigenfunctions
Important features of certain economic models may be revealed by studying positive eigenfunctions of appropriately chosen linear operators. Examples include long-run risk-return relationships in dynamic asset pricing models and components of marginal utility in external habit formation models. This paper provides identification conditions for positive eigenfunctions in nonparametric models. Identification is achieved if the operator satisfies two mild positivity conditions and a power compactness condition. Both existence and identification are achieved under a further non-degeneracy condition. The general results are applied to obtain new identification conditions for external habit formation models and for positive eigenfunctions of pricing operators in dynamic asset pricing models.
- Sprache
-
Englisch
- Erschienen in
-
Series: cemmap working paper ; No. CWP37/14
- Klassifikation
-
Wirtschaft
Estimation: General
Semiparametric and Nonparametric Methods: General
Financial Econometrics
- Thema
-
Nonparametric identification
Nonparametric models
Asset pricing
Markov processes
Perron-Frobenius theory
Shape restrictions
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Christensen, Timothy M.
- Ereignis
-
Veröffentlichung
- (wer)
-
Centre for Microdata Methods and Practice (cemmap)
- (wo)
-
London
- (wann)
-
2014
- DOI
-
doi:10.1920/wp.cem.2014.3714
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:44 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Christensen, Timothy M.
- Centre for Microdata Methods and Practice (cemmap)
Entstanden
- 2014
Ähnliche Objekte (12)
Nonparametric Identification of Discrete Choice Models
Nonparametric identification under discrete variation
Local identification of nonparametric and semiparametric models
Local identification of nonparametric and semiparametric models
Nonparametric Identification and Estimation of Multivariate Mixtures
Nonparametric Euler equation identification and estimation
Nonparametric identification in panels using quantiles
Nonparametric identification in panels using quantiles
Nonparametric identification with discrete endogenous variables
Proxy Variables and Nonparametric Identification of Causal Effects
Proxy variables and nonparametric identification of causal effects
Nonparametric identification of unobserved technological heterogeneity in production
Nonparametric Identification of Discrete Choice Models
Nonparametric identification under discrete variation
Local identification of nonparametric and semiparametric models
Local identification of nonparametric and semiparametric models
Nonparametric Identification and Estimation of Multivariate Mixtures
Nonparametric Euler equation identification and estimation
Nonparametric identification in panels using quantiles
Nonparametric identification in panels using quantiles
Nonparametric identification with discrete endogenous variables
Proxy Variables and Nonparametric Identification of Causal Effects
Proxy variables and nonparametric identification of causal effects
Nonparametric identification of unobserved technological heterogeneity in production
Nonparametric Identification of Discrete Choice Models
Nonparametric identification under discrete variation
Local identification of nonparametric and semiparametric models
Local identification of nonparametric and semiparametric models
Nonparametric Identification and Estimation of Multivariate Mixtures
Nonparametric Euler equation identification and estimation
Nonparametric identification in panels using quantiles
Nonparametric identification in panels using quantiles
Nonparametric identification with discrete endogenous variables
Proxy Variables and Nonparametric Identification of Causal Effects
Proxy variables and nonparametric identification of causal effects