Efficient importance sampling for ML estimation of SCD models

DOI10.1016/j.csda.2008.02.014zbMath1453.62040OpenAlexW2122081358MaRDI QIDQ961389

B. E. Eshmatov

Publication date: 30 March 2010

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: http://sites.uclouvain.be/econ/DP/IRES/2007-32.pdf

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Computational methods for problems pertaining to statistics (62-08) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)

Related Items

Efficient importance sampling in mixture frameworks, A flexible and automated likelihood based framework for inference in stochastic volatility models, Modeling dynamic effects of promotion on interpurchase times, Particle efficient importance sampling, Estimating stochastic volatility models using realized measures, Efficient importance sampling maximum likelihood estimation of stochastic differential equations, Improving MCMC, using efficient importance sampling, Inverse Gaussian Distribution for Modeling Conditional Durations in Finance, Bayesian inference of asymmetric stochastic conditional duration models, Simulation-Based Estimation Methods for Financial Time Series Models

Cites Work

• Unnamed Item
• Efficient high-dimensional importance sampling
• Bayesian analysis of the stochastic conditional duration model
• Time series of count data: Modeling, estimation and diagnostics
• The stochastic conditional duration model: a latent variable model for the analysis of financial durations
• Econometric modelling of stock market intraday activity.
• Monte Carlo maximum likelihood estimation for non-Gaussian state space models
• Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data