Arbeitspapier
Fast Efficient Importance Sampling by State Space Methods
We show that efficient importance sampling for nonlinear non-Gaussian state space models can be implemented by computationally efficient Kalman filter and smoothing methods. The result provides some new insights but it primarily leads to a simple and fast method for efficient importance sampling. A simulation study and empirical illustration provide some evidence of the computational gains.
- Sprache
-
Englisch
- Erschienen in
-
Series: Tinbergen Institute Discussion Paper ; No. 12-008/4
Klassifikation
Wirtschaft
Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
Model Construction and Estimation
Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
Model Construction and Estimation
Thema
Kalman filter
Monte Carlo maximum likelihood
Simulation smoothing
Zustandsraummodell
Maximum-Likelihood-Methode
Monte-Carlo-Methode
Simulation
Theorie
Monte Carlo maximum likelihood
Simulation smoothing
Zustandsraummodell
Maximum-Likelihood-Methode
Monte-Carlo-Methode
Simulation
Theorie
Ereignis
Geistige Schöpfung
(wer)
Koopman, Siem Jan
Nguyen, Thuy Minh
Nguyen, Thuy Minh
Ereignis
Veröffentlichung
(wer)
Tinbergen Institute
(wo)
Amsterdam and Rotterdam
(wann)
2012
- Handle
- Letzte Aktualisierung
-
20.09.2024, 08:23 MESZ
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.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Koopman, Siem Jan
- Nguyen, Thuy Minh
- Tinbergen Institute
Entstanden
- 2012
Ähnliche Objekte (12)
On Importance Sampling for State Space Models
Testing the assumptions behind importance sampling
Testing the assumptions behind importance sampling
Finding the European crime drop using a panel data model with stochastic trends
Time Series Modelling of Daily Tax Revenues
Convergence in European GDP Series
Time Series Models with a Common Stochastic Variance for Analysing Economic Time Series
Spline Smoothing over Difficult Regions
Extracting Business Cycles using Semi-parametric Time-varying Spectra with Applications to US Macroeconomic Time Series
Seasonality with Trend and Cycle Interactions in Unobserved Components Models
Forecasting Daily Time Series using Periodic Unobserved Components Time Series Models
Measuring Asymmetric Stochastic Cycle Components in U.S. Macroeconomic Time Series
On Importance Sampling for State Space Models
Testing the assumptions behind importance sampling
Testing the assumptions behind importance sampling
Finding the European crime drop using a panel data model with stochastic trends
Time Series Modelling of Daily Tax Revenues
Convergence in European GDP Series
Time Series Models with a Common Stochastic Variance for Analysing Economic Time Series
Spline Smoothing over Difficult Regions
Extracting Business Cycles using Semi-parametric Time-varying Spectra with Applications to US Macroeconomic Time Series
Seasonality with Trend and Cycle Interactions in Unobserved Components Models
Forecasting Daily Time Series using Periodic Unobserved Components Time Series Models
Measuring Asymmetric Stochastic Cycle Components in U.S. Macroeconomic Time Series
On Importance Sampling for State Space Models
Testing the assumptions behind importance sampling
Testing the assumptions behind importance sampling
Finding the European crime drop using a panel data model with stochastic trends
Time Series Modelling of Daily Tax Revenues
Convergence in European GDP Series
Time Series Models with a Common Stochastic Variance for Analysing Economic Time Series
Spline Smoothing over Difficult Regions
Extracting Business Cycles using Semi-parametric Time-varying Spectra with Applications to US Macroeconomic Time Series
Seasonality with Trend and Cycle Interactions in Unobserved Components Models
Forecasting Daily Time Series using Periodic Unobserved Components Time Series Models