How to avoid incorrect inferences (while gaining correct ones) in dynamic models

Andrew Q. Philips

Political Science Research and Methods, 2022, vol. 10, issue 4, 879-889

Abstract: A flurry of current interest in time series has focused on clarifying equation balance, fractional integration, and cointegration testing. Despite this, a number of recent suggestions may continue to lead scholars toward incorrect inferences. In this comment, I investigate the likelihood of drawing both correct and incorrect inferences under a variety of stationary and non-stationary data-generating processes. I extend previous work in this area by focusing on both short- and long-run effects using several popular model specifications. Given these findings, I conclude by offering a variety of recommendations to practitioners about how they can best specify their model.

Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.cambridge.org/core/product/identifier/ ... type/journal_article link to article abstract page (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cup:pscirm:v:10:y:2022:i:4:p:879-889_15

Access Statistics for this article

More articles in Political Science Research and Methods from Cambridge University Press Cambridge University Press, UPH, Shaftesbury Road, Cambridge CB2 8BS UK.
Bibliographic data for series maintained by Kirk Stebbing ().