BIVPOISSON: Stata module to perform seemingly unrelated count regression

Abbie Zhang, James Fisher () and Joseph Terza
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James Fisher: NA

Statistical Software Components from Boston College Department of Economics

Abstract: bivpoisson implements the count-valued seemingly unrelated regression (count SUR) estimator proposed in Terza and Zhang (2021). This paper shows that bivpoisson affords greater precision and accuracy than Linear Seemingly Unrelated Regression (sureg) when the underlying data are correlated and count-valued; see Terza and Zhang (2021, https://doi.org/10.7912/C2/2873) for details and illustrations. Post-Estimation command (in development) associated with this package will support predictions and causal effects parameter estimation (i.e., Average Treatment Effects).

Language: Stata
Requires: Stata version 17
Keywords: correlated count data; bivariate Poisson; seemingly unrelated regression, causal inference (search for similar items in EconPapers)
Date: 2022-07-22, Revised 2022-08-16
Note: This module should be installed from within Stata by typing "ssc install bivpoisson". The module is made available under terms of the GPL v3 (https://www.gnu.org/licenses/gpl-3.0.txt). Windows users should not attempt to download these files with a web browser.
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http://fmwww.bc.edu/repec/bocode/b/bivpoisson.sthlp help file (text/plain)
http://fmwww.bc.edu/repec/bocode/b/bivpoisson.ado program code (text/plain)
http://fmwww.bc.edu/repec/bocode/b/bivpoisson-example-code.do sample code (text/plain)
http://fmwww.bc.edu/repec/bocode/h/Health_Data.dta sample data file (application/x-stata)

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