Time aggregation of mixed causal–noncausal models

IF 2.1 4区经济学 Q2 ECONOMICS

Economics Letters Pub Date : 2024-10-11 DOI:10.1016/j.econlet.2024.112019

Sean Telg

引用次数: 0

Abstract

We study systematic and flow aggregation of mixed causal-noncausal autoregressive models. We show that aggregation preserves noncausality and generates a moving average component. Monte Carlo simulations demonstrate that backward- and forward-looking behavior can be identified empirically for sufficiently large samples.

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因果-非因果混合模型的时间聚合

我们研究了混合因果-非因果自回归模型的系统聚合和流动聚合。我们证明，聚合可以保持非因果性，并产生移动平均成分。蒙特卡罗模拟证明，在足够大的样本中，可以根据经验识别后向和前向行为。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Economics Letters ECONOMICS-

CiteScore

3.20

自引率

5.00%

发文量

348

审稿时长

30 days

期刊介绍： Many economists today are concerned by the proliferation of journals and the concomitant labyrinth of research to be conquered in order to reach the specific information they require. To combat this tendency, Economics Letters has been conceived and designed outside the realm of the traditional economics journal. As a Letters Journal, it consists of concise communications (letters) that provide a means of rapid and efficient dissemination of new results, models and methods in all fields of economic research.