gibbs_admkr_nw: Estimating bandwidths of the regressors

In bbemkr: Bayesian bandwidth estimation for multivariate kernel regression with Gaussian error

Description

Implements the random-walk Metropolis algorithm to estimate the bandwidths of the regressors

Usage

gibbs_admkr_nw(xh, inicost, k, mutsizp, prob, data_x, data_y)

Arguments

xh	Log of square bandwidths in the regression function
inicost	Cost value
k	Iteration number
mutsizp	Step size of random-walk Metropolis algorithm
prob	Optimal convergence rate for drawing single or multiple parameters
data_x	Regressors
data_y	Response variable

Details

1) The log bandwidths of the regressors are initialized using the normal reference rule of Silverman (1986).

2) Conditioning on the variance parameter of the error density, we implement random-walk Metropolis algorithm to update the bandwidths, in order to achieve the minimum cost value.

3) The bandwidth of the kernel-form error density can be directly sampled.

4) Iterate steps 2) and 3) until the cost value is minimized.

5) Check the convergence of the parameters by examining the simulation inefficient factor (sif) value. The smaller the sif value is, the better convergence of the parameters is.

Value

x	Estimated bandwidths of the regression function
cost	Cost value, that is negative of log posterior
accept_h	Accept or reject. accept_h=1 indicates acceptance, while accept_h=0 indicates rejection.
mutsizp	Step size of the random-walk Metropolis algorithm

Author(s)

Han Lin Shang

References

X. Zhang and R. D. Brooks and M. L. King (2009) A Bayesian approach to bandwidth selection for multivariate kernel regression with an application to state-price density estimation, Journal of Econometrics, 153, 21-32.

B. W. Silverman (1986) Density Estimation for Statistics and Data Analysis. Chapman and Hall, New York.

See Also

mcmcrecord_admkr, logdensity_admkr, loglikelihood_admkr, logpriors_admkr, gibbs_admkr_erro

bbemkr documentation built on May 1, 2019, 10:53 p.m.