NONCAUSAL VECTOR AUTOREGRESSION
NONCAUSAL VECTOR AUTOREGRESSION
复制标题
非因果向量自回归
DOI：
10.1017/s0266466612000448
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发表时间：
2009
影响因子：
0.8
通讯作者：
Pentti Saikkonen
中科院分区：
文献类型：
--
作者：
Markku Lanne;Pentti Saikkonen
针对非高斯时间序列，提出了一种新的非因果向量自回归（VAR）模型。由于可识别性的原因，需要非高斯性的假设。假设误差分布属于一个相当一般的椭圆分布类，我们发展了极大似然估计和统计推断的渐近理论。我们认为，允许非因果关系是特别重要的经济应用，目前只使用传统的因果VAR模型。事实上，如果非因果关系被错误地忽略，使用因果VAR模型可能会产生次优预测和误导性的经济解释。因此，我们提出了一个区分因果关系和非因果关系的程序。这些方法通过利率数据的应用进行了说明。
In this paper, we propose a new noncausal vector autoregressive (VAR) model for non-Gaussian time series. The assumption of non-Gaussianity is needed for reasons of identifiability. Assuming that the error distribution belongs to a fairly general class of elliptical distributions, we develop an asymptotic theory of maximum likelihood estimation and statistical inference. We argue that allowing for noncausality is of particular importance in economic applications that currently use only conventional causal VAR models. Indeed, if noncausality is incorrectly ignored, the use of a causal VAR model may yield suboptimal forecasts and misleading economic interpretations. Therefore, we propose a procedure for discriminating between causality and noncausality. The methods are illustrated with an application to interest rate data.