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Sequential Bayesian bandwidth selection for multivariate kernel regression with applications
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DOI：10.1016/j.econmod.2022.105859.png)
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摘要
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A new Bayesian bandwidth selection procedure is proposed for nonparametric kernel estimates based on the sequential Monte Carlo method. Compared with the existing Bayesian bandwidth selector of Zhang et al. (2009), this new method can enhance the convergence to the global optimum with a substantially faster computation speed. In particular, the method offers an improved out-of-sample performance as shown by simulations. The bandwidth selector is applied to the option state price density, production function, and nonparametric relationship between oil and stock index returns; results indicate that our proposed method outperforms other methods in terms of the mean square error and log-likelihood in all applications.
Keyword:
Kernel estimate
Bandwidth selection
Sequential Monte Carlo
State price density
Production function
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4.7
论文数:
6.5K
被引数:
1.6W
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Nonparametric panel data model for crude oil and stock market prices in net oil importing countries.png)
ENERGY ECONOMICS
ENERGY ECONOMICSIF14.2
PREAI
分享收藏A Bayesian approach to bandwidth selection for multivariate kernel regression with an application to state-price density estimation用于多元核回归的带宽选择的贝叶斯方法及其在状态价格密度估计中的应用
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