How Granger’s time series methods can eliminate hypothesis testing & parameter-centric analyses

William M. Briggs

DOI: 10.63065/ajafe.2026.1.002

Email: matt@wmbriggs.com

Đơn vị công tác:

Ngày nhận bài: 09/09/2026

Ngày nhận bài sửa: 09/09/2026

Ngày duyệt đăng: 09/09/2026

Lượt xem: 10

Downloads: 0

Các định dạng trích dẫn

Tóm tắt

Purpose – Hypothesis testing and parameter-centric analyses ought to be replaced by the concept of relevance and usefulness. All models ought to be tested against reality, which almost never happens, and cannot when hypothesis testing or parameter statements are used.

Design/methodology/approach – We review the state of evidence in time series models, specifically Granger causality and testing, and see why this was seen as an innovation. We show that the central idea of prediction applies to all models, not just time series.

Findings – Granger so-called causality does not identify cause, nor does hypothesis testing or parameter statements. But the history of Granger’s idea show the shortcomings of classical analysis methods and point the way toward clear thinking on cause.

Originality/value – If hypothesis testing and parameter-centric analysis could be abandoned in favor of predictive approaches, we could eliminate a major source of error and over-certainty.

Từ khóa

Causality, Granger Testing, Hypothesis Testing, Predictive Modeling, Time Series

Download Mua ngay Buy now

Tài liệu tham khảo

William M. Briggs. Uncertainty: The Soul of Probability, Modeling & Statistics. Springer, New York, 2016.

William M. Briggs and Hung T Nguyen. Clarifying ASA’s views on p-values in hypothesis testing. Asian Journal of Economics and Banking, 3:1–16, 2019.

Edward Feser. Aristotle’s Revenge: The Metaphysical Foundations of Physical and Biological Science. Editions Scholasticae, Neunkirchen-Seelscheid, Germany, 2014.

Tilmann Gneiting and Adrian E. Raftery. Strictly proper scoring rules, prediction, and estimation. JASA, 102:359–378, 2007.

C.W.J. Granger. Investigating causal relations by econometric models and cross-spectral methods. Econometrics, 37:424–438, 1969.

Ali Shojaie and Emily B. Fox. Granger causality: A review and recent advances. Annual Review of Statistics and its Application, 9:289–313, 2022.