Time aggregation of mixed causal–noncausal models

Sean Telg

doi:10.1016/j.econlet.2024.112019

Time aggregation of mixed causal–noncausal models

Sean Telg^*

^*Corresponding author for this work

Research output: Contribution to Journal › Article › Academic › peer-review

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.

Original language	English
Article number	112019
Number of pages	3
Journal	Economics Letters
Volume	244
DOIs	https://doi.org/10.1016/j.econlet.2024.112019
Publication status	Published - Nov 2024

Bibliographical note

Publisher Copyright:
© 2024 The Author

Keywords

MAR models
Non-gaussianity
Noncausality
Systematic and flow aggregation

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10.1016/j.econlet.2024.112019

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title = "Time aggregation of mixed causal–noncausal models",

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.",

keywords = "MAR models, Non-gaussianity, Noncausality, Systematic and flow aggregation",

author = "Sean Telg",

note = "Publisher Copyright: {\textcopyright} 2024 The Author",

year = "2024",

month = nov,

doi = "10.1016/j.econlet.2024.112019",

language = "English",

volume = "244",

journal = "Economics Letters",

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publisher = "Elsevier B.V.",

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T1 - Time aggregation of mixed causal–noncausal models

AU - Telg, Sean

PY - 2024/11

Y1 - 2024/11

N2 - 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.

AB - 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.

KW - MAR models

KW - Non-gaussianity

KW - Noncausality

KW - Systematic and flow aggregation

UR - https://www.scopus.com/pages/publications/85206528966

UR - https://www.scopus.com/pages/publications/85206528966#tab=citedBy

U2 - 10.1016/j.econlet.2024.112019

DO - 10.1016/j.econlet.2024.112019

M3 - Article

AN - SCOPUS:85206528966

SN - 0165-1765

VL - 244

JO - Economics Letters

JF - Economics Letters

M1 - 112019

ER -

Time aggregation of mixed causal–noncausal models

Abstract

Bibliographical note

Keywords

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