Expansion and estimation of Lévy process functionals in nonlinear and nonstationary time series regression
提出一种级数估计方法，用于估计计量经济时间序列模型中Lévy过程的未知时变泛函，并建立了双变量泛函部分和的一般渐近理论，模拟验证了有限样本性能。
In this article, we develop a series estimation method for unknown time-inhomogeneous functionals of Lévy processes involved in econometric time series models. To obtain an asymptotic distribution for the proposed estimators, we establish a general asymptotic theory for partial sums of bivariate functionals of time and nonstationary variables. These results show that the proposed estimators in different situations converge to quite different random variables. In addition, the rates of convergence depend on various factors rather than just the sample size. Finite sample simulations are provided to evaluate the finite sample performance of the proposed model and estimation method.