Search Results - subject:"Nonlinear State Space"

Measuring Asymmetric Stochastic Cycle Components in U.S. Macroeconomic Time Series

Koopman, Siem Jan; Lee, Kai Ming - 2005

To gain insights in the current status of the economy, macroeconomic time series are often decomposed into trend, cycle and irregular components. This can be done by nonparametric band-pass filtering methods in the frequency domain or by model-based decompositions based on autoregressive moving...

Persistent link: https://www.econbiz.de/10010325334

Measuring Asymmetric Stochastic Cycle Components in U.S. Macroeconomic Time Series

Koopman, Siem Jan; Lee, Kai Ming - Tinbergen Institute - 2005

Persistent link: https://www.econbiz.de/10005137023

Measuring Asymmetric Stochastic Cycle Components in U.S. Macroeconomic Time Series

Koopman, Siem Jan; Lee, Kai Ming - Tinbergen Instituut - 2005

Persistent link: https://www.econbiz.de/10011256217

Measuring asymmetric stochastic cycle components in US macroeconomic time series

Koopman, Siem Jan; Lee, Kai Ming - 2005

Persistent link: https://www.econbiz.de/10011346480

On Leverage in a Stochastic Volatility Model

Yu, Jun - School of Economics, Singapore Management University - 2004

Rossi (2004, Journal of Econometrics, forthcoming). Using a Gaussian nonlinear state space form with uncorrelated …

Persistent link: https://www.econbiz.de/10005091188

Estimation of Stochastic Volatility Models : An Approximation to the Nonlinear State Space

Shimada, Junji; Tsukuda, Yoshihiko - Econometric Society - 2004

) method to the nonlinear state space representation, and to show that the LA method is workable for estimating the SV models … state space model. The LA method approximates the logarithm of the joint density of current observation and volatility … including the multivariate SV model and the dynamic bivariate mixture (DBM) model. The SV model can be regarded as a nonlinear …

Persistent link: https://www.econbiz.de/10005702767

Bayesian Estimation of a Stochastic Volatility Model Using Option and Spot Prices: Application of a Bivariate Kalman Filter

Forbes, Catherine S.; Martin, Gael M.; Wright, Jill - Department of Econometrics and Business Statistics, … - 2003

In this paper Bayesian methods are applied to a stochastic volatility model using both the prices of the asset and the prices of options written on the asset. Posterior densities for all model parameters, latent volatilities and the market price of volatility risk are produced via a hybrid...

Persistent link: https://www.econbiz.de/10005149095

Simulation-Based Bayesian Estimation of Affine Term Structure Models

Sanford, Andrew D.; Martin, Gael M. - Department of Econometrics and Business Statistics, … - 2003

This paper demonstrates the application of Bayesian simulation-based estimation to a class of interest rate models known as Affine Term Structure (ATS) models. The technique used is based on a Markov Chain Monte Carlo algorithm, with the discrete observations on yields augmented by additional...

Persistent link: https://www.econbiz.de/10005149102

Pooling and Dynamic Forgetting Effects in Multitheme Advertising: Tracking the Advertising Sales Relationship with Particle Filters

Bruce, Norris I. - In: Marketing Science 27 (2008) 4, pp. 659-673

Firms often use a pool or series of advertising themes in their campaigns. Thus, for example, a firm may employ some of its advertising to promote price-related themes or messages and other of its advertising to promote product-related themes. This study examines the interdependence that can...

Persistent link: https://www.econbiz.de/10008787687

Inference for a Class of Stochastic Volatility Models Using Option and Spot Prices: Application of a Bivariate Kalman Filter

Forbes, Catherine S.; Martin, Gael M.; Wright, Jill - In: Econometric Reviews 26 (2007) 2-4, pp. 387-418

nonlinear state space representation of the model. Crucially, information from both the spot and option prices affects the draws …

Persistent link: https://www.econbiz.de/10005644467