Generalized two-part fractional regression with cmp Article Swipe
Researchers who model fractional dependent variables often need to consider whether their data were generated by a two-part process. Two-part models are ideal for modeling two-part processes because they allow us to model the participation and magnitude decisions separately. While community-contributed commands currently facilitate estimation of two-part models, no specialized command exists for fitting two-part models with process dependency. In this article, I describe generalized two-part fractional regression, which allows for dependency between models’ parts. I show how this model can be fit using the community-contributed cmp command (Roodman, 2011, Stata Journal 11: 159–206). I use a data example on the financial leverage of firms to illustrate how cmp can be used to fit generalized two-part fractional regression. Furthermore, I show how to obtain predicted values of the fractional dependent variable and marginal effects that are useful for model interpretation. Finally, I show how to compute model fit statistics and perform the RESET test, which are useful for model evaluation.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1177/1536867x19854017
- https://journals.sagepub.com/doi/pdf/10.1177/1536867X19854017
- OA Status
- bronze
- Cited By
- 29
- References
- 16
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2954639974
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Raw OpenAlex JSON
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https://openalex.org/W2954639974Canonical identifier for this work in OpenAlex
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https://doi.org/10.1177/1536867x19854017Digital Object Identifier
- Title
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Generalized two-part fractional regression with cmpWork title
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articleOpenAlex work type
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enPrimary language
- Publication year
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2019Year of publication
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2019-06-01Full publication date if available
- Authors
-
Jesper WulffList of authors in order
- Landing page
-
https://doi.org/10.1177/1536867x19854017Publisher landing page
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-
https://journals.sagepub.com/doi/pdf/10.1177/1536867X19854017Direct link to full text PDF
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YesWhether a free full text is available
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bronzeOpen access status per OpenAlex
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https://journals.sagepub.com/doi/pdf/10.1177/1536867X19854017Direct OA link when available
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Leverage (statistics), Dependency (UML), Computer science, Regression analysis, Econometrics, Regression, Mathematics, Statistics, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
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29Total citation count in OpenAlex
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2025: 9, 2024: 7, 2023: 4, 2022: 6, 2021: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Researchers | 0 |
| abstract_inverted_index.dependency. | 58 |
| abstract_inverted_index.evaluation. | 159 |
| abstract_inverted_index.generalized | 64, 114 |
| abstract_inverted_index.regression, | 67 |
| abstract_inverted_index.regression. | 117 |
| abstract_inverted_index.separately. | 38 |
| abstract_inverted_index.specialized | 49 |
| abstract_inverted_index.Furthermore, | 118 |
| abstract_inverted_index.participation | 34 |
| abstract_inverted_index.interpretation. | 139 |
| abstract_inverted_index.community-contributed | 40, 85 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5017450391 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 1 |
| corresponding_institution_ids | https://openalex.org/I204337017 |
| citation_normalized_percentile.value | 0.75481504 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |