Predicted Probabilities and Inference with Multinomial Logit

Philip Paolino

Political Analysis, 2021, vol. 29, issue 3, 416-421

Abstract: Multinomial logit (MNL) differs from many other econometric methods because it estimates the effects of variables upon nominal, not ordered outcomes. One consequence of this is that the estimated coefficients vary depending upon a researcher’s decision about the choice of a reference, or “baseline,” outcome. Most researchers realize this in principle, but many focus upon the statistical significance of MNL coefficients for inference in the same way that they use the coefficients from models with ordered dependent variables. In some instances, this leads researchers to report statistics that do not reflect the correct quantities of interest and reach flawed conclusions. In this note, I argue that researchers need to orient their approach to analyzing both the substantive and statistical significance of predicted probabilities of interest that match their research questions.

Date: 2021
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:polals:v:29:y:2021:i:3:p:416-421_9

Access Statistics for this article

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