A command to estimate and interpret models of dynamic compositional dependent variables: New features for dynsimpie

Yoo Sun Jung (), Flávio D. S. Souza (), Andrew Q. Philips (), Amanda Rutherford () and Guy D. Whitten ()
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Yoo Sun Jung: University of California–San Diego
Flávio D. S. Souza: Texas A&M University
Andrew Q. Philips: University of Colorado Boulder
Amanda Rutherford: Indiana University
Guy D. Whitten: Texas A&M University

Stata Journal, 2020, vol. 20, issue 3, 584-603

Abstract: Philips, Rutherford, and Whitten (2016, Stata Journal 16: 662–677) introduced dynsimpie, a command to examine dynamic compositional dependent variables. In this article, we present an update to dynsimpie and three new ado- files: cfbplot, effectsplot, and dynsimpiecoef. These updates greatly enhance the range of models that can be estimated and the ways in which model results can now be presented. The command dynsimpie has been updated so that users can obtain both prediction plots and change-from-baseline plots using postestimation commands. With the new command dynsimpiecoef, various types of coefficient plots can also be obtained. We illustrate these improvements using monthly data on support for political parties in the United Kingdom.

Keywords: dynsimpie; cfbplot; effectsplot; dynsimpiecoef; time series; seemingly unrelated regression; cointegration; dynamic modeling; dynamic com- position; error correction; lagged dependent variable (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1177/1536867X20953570

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