We consider estimation of the linear component of a partial linear model when errors and regressors have long-range dependence. Assuming that errors and the stochastic component of regressors are linear processes with i.i.d. innovations, we closely examine the asymptotic properties of the OLS...

This paper studies the properties of the sieve bootstrap for a class of linear processes which exhibit strong dependence. The sieve bootstrap scheme is based on residual resampling from autoregressive approximations the order of which increases slowly with the sample size. The first-order...

This paper gives a selective overview on the functional coefficient models with their particular applications in economics and finance. Functional coefficient models are very useful analytic tools to explore complex dynamic structures and evolutions for functional data in various areas,...

Let Xt=∑j=0∞cjεt−j be a moving average process with GARCH (1, 1) innovations {εt}. In this paper, the asymptotic behavior of the quadratic form Qn=∑j=1n∑s=1nb(t−s)XtXs is derived when the innovation {εt} is a long-memory and heavy-tailed process with tail index α, where {b(i)} is...

We consider nonparametric estimation of the conditional qth quantile for stationary time series. We deal with stationary time series with strong time dependence and heavy tails under the setting of random design. We estimate the conditional qth quantile by local linear regression and investigate...