a note on identification of bivariate copulas for discrete count data
Clicks: 152
ID: 238939
2017
Article Quality & Performance Metrics
Overall Quality
Improving Quality
0.0
/100
Combines engagement data with AI-assessed academic quality
Reader Engagement
Emerging Content
0.3
/100
1 views
1 readers
Trending
AI Quality Assessment
Not analyzed
Abstract
Copulas have enjoyed increased usage in many areas of econometrics, including applications with discrete outcomes. However, Genest and Nešlehová (2007) present evidence that copulas for discrete outcomes are not identified, particularly when those discrete outcomes follow count distributions. This paper confirms the Genest and Nešlehová result using a series of simulation exercises. The paper then proceeds to show that those identification concerns diminish if the model has a regression structure such that the exogenous variable(s) generates additional variation in the outcomes and thus more completely covers the outcome domain.Reference Key |
trivedi2017econometricsa
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
---|---|
Authors | ;Pravin Trivedi;David Zimmer |
Journal | developmental cognitive neuroscience |
Year | 2017 |
DOI | 10.3390/econometrics5010010 |
URL | |
Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.