Block whittle estimation of time varying stochastic regression models with long memory

This paper proposes an estimator that accounts for time variation in a regression relationship with stochastic regressors exhibiting long-range dependence, covering weak fractional cointegration as a special case. An interesting application of this estimator is its ability to handle situations where the regression coefficient changes abruptly. The parametric formulation of this estimator is introduced using the Block-Whittle-based estimation. We analyze the asymptotic properties of this estimator, including consistency and asymptotic normality. Furthermore, we examine the finite sample behavior of the estimator through Monte Carlo simulations. Additionally, we consider a real-life application to demonstrate its advantages over the constant case.