Combined estimating function for random coefficient models with correlated errors

Abstract

ABSTRACTEstimating functionshave been shown to be convenient to study inference for non linear time series models. Recently, Thavaneswaran et al. (2012) used combined estimating functions to study inference for random coefficient autoregressive (RCA) models with generalized autoregressive heteroscedasticity errors. While most RCA modeling assumes that the random term and the error are independent, Chandra and Taniguchi (2001) studied inference for RCA models with correlated errors using linear estimating functions. In this paper, we derive the quadratic estimating functions for the joint estimation of the conditional mean, variance, and correlation parameters of the RCA models with correlated errors.