Arbeitspapier

Nonparametric stochastic discount factor decomposition

We introduce econometric methods to perform estimation and inference on the permanent and transitory components of the stochastic discount factor (SDF) in dynamic Markov environments. The approach is nonparametric in that it does not impose parametric restrictions on the law of motion of the state process. We propose sieve estimators of the eigenvalue-eigenfunction pair which are used to decompose the SDF into its permanent and transitory components, as well as estimators of the long-run yield and the entropy of the permanent component of the SDF, allowing for a wide variety of empirically relevant setups. Consistency and convergence rates are established. The estimators of the eigenvalue, yield and entropy are shown to be asymptotically normal and semiparametrically efficient when the SDF is observable. We also introduce nonparametric estimators of the continuation value under Epstein-Zin preferences, thereby extending the scope of our estimators to an important class of recursive preferences. The estimators are simple to implement, perform favorably in simulations, and may be used to numerically compute the eigenfunction and its eigenvalue in fully specified models when analytical solutions are not available.

Sprache
Englisch

Erschienen in
Series: cemmap working paper ; No. CWP24/15

Klassifikation
Wirtschaft
Estimation: General
Semiparametric and Nonparametric Methods: General
Financial Econometrics
Thema
Nonparametric estimation
Sieve estimation
Stochastic discount factor
Permanenttransitory decomposition
Long run
Entropy

Ereignis
Geistige Schöpfung
(wer)
Christensen, Timothy
Ereignis
Veröffentlichung
(wer)
Centre for Microdata Methods and Practice (cemmap)
(wo)
London
(wann)
2015

DOI
doi:10.1920/wp.cem.2015.2415
Handle
Letzte Aktualisierung
10.03.2025, 11:44 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Christensen, Timothy
  • Centre for Microdata Methods and Practice (cemmap)

Entstanden

  • 2015

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