Maximum Likelihood Estimation of Models with Beta-Distributed Dependent Variables
Bibliographic Data
| ID | 7971014 |
|---|---|
| Authors | Philip Paolino (0000-0002-5452-3902, The University of Texas at Austin, corresponding author) |
| Year | 2001 |
| Volume | 9 |
| Issue | 4 |
| Pages | 325-346 |
| Publication date | 2001-01-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Political Analysis (JOURNAL) |
| Journal identifiers | ISSN: 1047-1987 • E-ISSN: 1476-4989 |
| Publisher | Cambridge University Press (CUP) (PUBLISHER) |
| DOI | 10.1093/oxfordjournals.pan.a004873 |
| OpenAlex | W1989537995 |
| Language | EN |
| Citations received | 31 |
| References cited | 16 |
Research in political science is often concerned with modeling dependent variables that are proportions. Proportions are relevant in a wide variety of substantive areas, including elections, the bureaucracy, and interest groups. Yet because most researchers rely upon an approach, OLS, that does not recognize key aspects of proportions, the conclusions we reach from normal models may not provide the best understanding of phenomena of interest in these areas. In this paper, I use Monte Carlo simulations to show that maximum likelihood estimation of these data using the beta distribution may provide more accurate and more precise results. I then present empirical analyses illustrating some of these differences
BETA (programming language · Bureaucracy · Econometrics · Economics · Estimation · Maximum likelihood · Monte Carlo method · Political science · Politics · Statistics · Variety (cybernetics · Computer Science · Electoral Systems and Political Participation · Game Theory and Voting Systems · Mathematics · Political Influence and Corporate Strategies
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| Unique citing works | 31 |
|---|---|
| Citations per year | 1,35 |
| Citation span | 2003 - 2025 (23) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 31 |