A Test for model specification of diffusion processes
By S. Chen, Jiti Gao, Cheng Yong Tang. 2007
81 citations recorded by the source corpus.
We propose a test for model specification of a parametric diffusion process based on a kernel estimation of the transitional density of the process. The empirical likelihood is used to formulate a statistic, for each kernel smoothing bandwidth, which is effectively a Studentized L2-distance between the kernel transitional density estimator and the parametric transitional density implied by the parametric process. To reduce the sensitivity of the test on smoothing bandwidth choice, the final test statistic is constructed by combining the empirical likelihood statistics over a set of smoothing bandwidths. To better capture the finite sample distribution of the test statistic and data dependence, the critical value of the test is obtained by a parametric bootstrap procedure. Properties of the test are evaluated asymptotically and numerically by simulation and by a real data example.
Subfields: Finance, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/6200bfddba9defe7c44d896043f467640ccb3c79
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Citations in the corpus
- Nonparametric Transition-Based Tests for Jump Diffusions (2009, unknown): “Chen, Gao, and Tang (2008) applied an integrated empirical likelihood method, addressed the issue of bandwidth selection, and derived the asymptotic null distribution.”
- An updated review of Goodness-of-Fit tests for regression models (2013, methodology): “A test for a parametric model specification, based on kernel estimation and using the ideas of empirical likelihood, was proposed by Chen et al. (2008).”