Robust bootstrap estimates in heteroscedastic semi-varying coefficient models and applications in analyzing Australia CPI data

ABSTRACT

This article deals with the estimation of the parametric component, which is of primary interest, in the heteroscedastic semi-varying coefficient models. Based on the bootstrap technique, we present a procedure for estimating the parameters, which can provide a reliable approximation to the asymptotic distribution of the profile least-square (PLS) estimator. Furthermore, a bootstrap-type estimator of covariance matrix is developed, which is proved to be a consistent estimator of the covariance matrix. Moreover, some simulation experiments are conducted to evaluate the finite sample performance for the proposed methodology. Finally, the Australia CPI dataset is analyzed to demonstrate the application of the methods.

KEYWORDS:

• Heteroscedasticity
• Profile least-squares
• Robust bootstrap estimator
• Semi-varying coefficient models

MATHEMATICS SUBJECT CLASSIFICATION:

• Primary 62H12
• Secondary 62A10

Funding

The research work is supported by the National Natural Science Foundation of China under Grant No. 11571073, 11401094, the Natural Science Foundation of Jiangsu Province of China under Grant No. BK20140617, BK20141326 the Research Fund for the Doctoral Program of Higher Education of 315 China under Grant No. 20120092110021, and the Talent Introduction Project of Nanjing Audit University.