Journal article | Zeitschriftenartikel
Testing the assumptions behind importance sampling
Importance sampling is used in many areas of modern econometrics to approximate unsolvable integrals. Its reliable use requires the sampler to possess a variance, for this guarantees a square root speed of convergence and asymptotic normality of the estimator of the integral. However, this assumption is seldom checked. In this paper we use extreme value theory to empirically assess the appropriateness of this assumption. Our main application is the stochastic volatility model, where importance sampling is commonly used for maximum likelihood estimation of the parameters of the model.
- Extent
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Seite(n): 2-11
- Language
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Englisch
- Notes
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Status: Postprint; begutachtet (peer reviewed)
- Bibliographic citation
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Journal of Econometrics, 149(1)
- Subject
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Wirtschaft
Wirtschaftsstatistik, Ökonometrie, Wirtschaftsinformatik
Simulation
- Event
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Geistige Schöpfung
- (who)
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Koopman, Siem Jan
Shephard, Neil
Creal, Drew
- Event
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Veröffentlichung
- (where)
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Niederlande
- (when)
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2009
- DOI
- URN
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urn:nbn:de:0168-ssoar-238988
- Rights
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GESIS - Leibniz-Institut für Sozialwissenschaften. Bibliothek Köln
- Last update
-
21.06.2024, 4:26 PM CEST
Data provider
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GESIS - Leibniz-Institut für Sozialwissenschaften. Bibliothek Köln. If you have any questions about the object, please contact the data provider.
Object type
- Zeitschriftenartikel
Associated
- Koopman, Siem Jan
- Shephard, Neil
- Creal, Drew
Time of origin
- 2009
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Numerically Accelerated Importance Sampling for Nonlinear Non-Gaussian State Space Models
Forecasting economic time series using score-driven dynamic models with mixed-data sampling
Forecasting the U.S. Term Structure of Interest Rates using a Macroeconomic Smooth Dynamic Factor Model
Beta observation-driven models with exogenous regressors: a joint analysis of realized correlation and leverage effects
Bayesian Dynamic Modeling of High-Frequency Integer Price Changes
Convergence in European GDP Series
Likelihood-based Analysis for Dynamic Factor Models
A Forty Year Assessment of Forecasting the Boat Race
Finding the European crime drop using a panel data model with stochastic trends
Testing the assumptions behind importance sampling
On Importance Sampling for State Space Models
Fast Efficient Importance Sampling by State Space Methods
Numerically Accelerated Importance Sampling for Nonlinear Non-Gaussian State Space Models
Forecasting economic time series using score-driven dynamic models with mixed-data sampling
Forecasting the U.S. Term Structure of Interest Rates using a Macroeconomic Smooth Dynamic Factor Model
Beta observation-driven models with exogenous regressors: a joint analysis of realized correlation and leverage effects
Bayesian Dynamic Modeling of High-Frequency Integer Price Changes
Convergence in European GDP Series
Likelihood-based Analysis for Dynamic Factor Models
A Forty Year Assessment of Forecasting the Boat Race