Bandwidth Issues White Papers
A Bayesian Approach to Bandwidth Selection for Multivariate Kernel Regression With an Application to State-Price Density Estimation
Overview Multivariate kernel regression is an important tool for investigating the relationship between a response and a set of explanatory variables. It is generally accepted that the performance of a kernel regression estimator largely depends on the choice of bandwidth rather than the kernel function. This nonparametric technique has been employed in a number of empirical studies including the state-price density estimation pioneered by Ait-Sahalia and Lo (1998). However, the widespread usefulness of multivariate kernel regression has been limited by the difficulty in computing a data-driven bandwidth. This paper presents a Bayesian approach to bandwidth selection for multivariate kernel regression. A Markov chain Monte Carlo algorithm is presented to sample the bandwidth vector and other parameters in a multivariate kernel regression model.
| Publisher | Monash University | File Format | |
|---|---|---|---|
| Date Published | November 2007 | ||
| Format | White Papers | ||
| Topics | |||
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