Univariate Versus Multivariate Modeling of Panel Data: Model Specification and Goodness-of-Fit Testing
Ver/ Abrir
Metadatos
Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/8645
comunitat-uji-handle3:10234/8646
comunitat-uji-handle4:
INVESTIGACIONMetadatos
Título
Univariate Versus Multivariate Modeling of Panel Data: Model Specification and Goodness-of-Fit TestingFecha de publicación
2018Editor
SAGE PublicationsISSN
1094-4281; 1552-7425Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
http://journals.sagepub.com/doi/abs/10.1177/1094428117715509Versión
info:eu-repo/semantics/acceptedVersionPalabras clave / Materias
Resumen
Two approaches are commonly in use for analyzing panel data: the univariate, which arranges data in
long format and estimates just one regression equation; and the multivariate, which arranges data in
wide format, ... [+]
Two approaches are commonly in use for analyzing panel data: the univariate, which arranges data in
long format and estimates just one regression equation; and the multivariate, which arranges data in
wide format, and simultaneously estimates a set of regression equations. Although technical articles
relating the two approaches exist, they do not seem to have had an impact in organizational
research. This article revisits the connection between the univariate and multivariate approaches,
elucidating conditions under which they yield the same—or similar—results, and discusses their
complementariness. The article is addressed to applied researchers. For those familiar only with the
univariate approach, it contributes with conceptual simplicity on goodness-of-fit testing and a variety
of tests for misspecification (Hausman test, heteroscedasticity, autocorrelation, etc.), and simplifies
expanding the model to time-varying parameters, dynamics, measurement error, and so on. For all
practitioners, the comparative and side-by-side analyses of the two approaches on two data sets—
demonstration data and empirical data with missing values—contributes to broadening their
perspective of panel data modeling and expanding their tools for analyses. Both univariate and
multivariate analyses are performed in Stata and R. [-]
Publicado en
Organizational Research Methods 2018, Vol. 21(1)Proyecto de investigación
ECO2015-66671-P ; ECO2014-59885-PDerechos de acceso
Aparece en las colecciones
- EMP_Articles [451]