Abstract
Continuous-time autoregressive (CAR) model is very powerful when modeling many real world continuous processes. When the model is driven by Brownian motion, parameter inference is usually based on the likelihood calculation using the Kalman filter; while the model is driven by non-Gaussian Levy process, Monte Carlo type of methods are often applied to approximate the likelihood. In both cases, likelihood evaluation is the key but is not always easy. Here we propose an innovative Bayesian inference method without the requirement of likelihood evaluation. The algorithm is in a framework of approximate Bayesian computation (ABC). Distance correlation is employed as a very flexible summary statistics for ABC and the p-value calculated from distance correlation provides a good measurement of the dependence between generated samples. Simulation study shows that this approach is straightforward and effective in inferring CAR model parameters.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
