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首页> 外文期刊>Journal of Time Series Analysis >ASYMPTOTICS FOR THE CONDITIONAL-SUM-OF-SQUARES ESTIMATOR IN MULTIVARIATE FRACTIONAL TIME-SERIES MODELS
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ASYMPTOTICS FOR THE CONDITIONAL-SUM-OF-SQUARES ESTIMATOR IN MULTIVARIATE FRACTIONAL TIME-SERIES MODELS

机译：多元分数阶时间模型中条件求和估计的渐近性

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摘要

This article proves consistency and asymptotic normality for the conditional-sum-of-squares estimator, which is equivalent to the conditional maximum likelihood estimator, in multivariate fractional time-series models. The model is parametric and quite general and, in particular, encompasses the multivariate non-cointegrated fractional autoregressive integrated moving average (ARIMA) model. The novelty of the consistency result, in particular, is that it applies to a multivariate model and to an arbitrarily large set of admissible parameter values, for which the objective function does not converge uniformly in probability, thus making the proof much more challenging than usual. The neighbourhood around the critical point where uniform convergence fails is handled using a truncation argument.
机译：本文证明了多元分数时间序列模型中条件平方和估计的一致性和渐近正态性，它等于条件最大似然估计。该模型是参数化且相当通用的模型，特别是包含多元非协整分数自回归积分移动平均值（ARIMA）模型。一致性结果的新颖性尤其在于它适用于多变量模型和任意大的一组可接受的参数值，对于这些参数值，目标函数的概率并不一致地收敛，因此使证明比平时更具挑战性。使用截断参数处理统一收敛失败的临界点附近的邻域。

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