Continuous Record Asymptotics in Systems of Stochastic Differential Equations
研究了基于离散观测数据的估计问题，通过将离散过程嵌入连续时间过程，推导了当采样频率趋于零而数据跨度固定时的渐近性质，适用于近单位根过程。
This paper considers estimation based on a set of T + 1 discrete observations, y (0), y ( h ), y (2 h ),…, y ( Th ) = y ( N ), where h is the sampling frequency and N is the span of the data. In contrast to the standard approach of driving N to infinity for a fixed sampling frequency, the current paper follows Phillips [35,36] and Perron [29] and examines the “dual” asymptotics implied by letting h tend to zero while the span N remains fixed. We suggest a way of explicitly embedding discrete processes into continuous-time processes, and using this approach we generalize the results of the above-mentioned authors and derive continuous record asymptotics for vector first-order processes with positive roots in a neighborhood of one and we also consider the case of a scalar second-order process. We illustrate the method by two examples. The first example is a near unit root model with drift and trend.