Weighted least squares-based inference for stable and unstable threshold power ARCH processes

We establish consistency and asymptotic normality for a weighted least squares estimate (2SWLSE) of a threshold power ARCH process in both stationary and nonstationary environments. For models with heavy tail innovations, the 2SWLSE is more efficient than the quasi-maximum likelihood estimate.