There has been considerable attention paid to estimation of conditional variance functions in the literature. We propose a nonparametric model for the conditional covariance matrix. A kernel estimator is developed, its asymptotic bias and variance are derived, and its asymptotic normality is established. A data example is used to illustrate the proposed procedure.
|Original language||English (US)|
|Number of pages||11|
|State||Published - Jan 1 2010|
All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Statistics, Probability and Uncertainty