Conditional variance estimation in regression models with long memory

On conditional variance estimation in nonparametric regression

In this paper we consider a nonparametric regression model in which the conditional variance function is assumed to vary smoothly with the predictor. We offer an easily implemented and fully Bayesian approach that involves the Markov chain Monte Carlo sampling of standard distributions. This method is based on a technique utilized by Kim, Shephard, and Chib (1998) for the stochastic volatility ...

Variance Function Estimation in Heteroscedastic Regression Models

Variance Estimation in Time Series Regression Models

Variance Estimation in High Dimensional Regression Models

We treat the problem of variance estimation of the least squares estimate of the parameter in high dimensional linear regression models by using the Uncorrelated Weights Bootstrap (UBS). We find a representation of the UBS dispersion matrix and show that the bootstrap estimator is consistent if p/n → 0 where p is the dimension of the parameter and n is the sample size. For fixed dimension we sh...

Minimum distance conditional variance function checking in heteroscedastic regression models