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

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...

Full description

Saved in:
Bibliographic Details
Published in:Political science research and methods 2022-10, Vol.10 (4), p.879-889
Main Author: Philips, Andrew Q.
Format: Article
Language:English
Subjects:
Dependent variables
Independent variables
Political science
Regression analysis
Research Note
Sovereign debt
Time series
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2049-8470
2049-8489
DOI:10.1017/psrm.2021.31