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dc.contributor.authorZyphur, Michael Jen_UK
dc.contributor.authorHamaker, Ellen Len_UK
dc.contributor.authorTay, Louisen_UK
dc.contributor.authorVoelkle, Manuelen_UK
dc.contributor.authorPreacher, Kristopher Jen_UK
dc.contributor.authorZhang, Zhenen_UK
dc.contributor.authorAllison, Paul Den_UK
dc.contributor.authorPierides, Dean Cen_UK
dc.contributor.authorKoval, Peteren_UK
dc.contributor.authorDiener, Edward Fen_UK
dc.description.abstractThis article describes some potential uses of Bayesian estimation for time-series and panel data models by incorporating information from prior probabilities (i.e., priors) in addition to observed data. Drawing on econometrics and other literatures we illustrate the use of informative “shrinkage” or “small variance” priors (including so-called “Minnesota priors”) while extending prior work on the general cross-lagged panel model (GCLM). Using a panel dataset of national income and subjective well-being (SWB) we describe three key benefits of these priors. First, they shrink parameter estimates toward zero or toward each other for time-varying parameters, which lends additional support for an income → SWB effect that is not supported with maximum likelihood (ML). This is useful because, second, these priors increase model parsimony and the stability of estimates (keeping them within more reasonable bounds) and thus improve out-of-sample predictions and interpretability, which means estimated effect should also be more trustworthy than under ML. Third, these priors allow estimating otherwise under-identified models under ML, allowing higher-order lagged effects and time-varying parameters that are otherwise impossible to estimate using observed data alone. In conclusion we note some of the responsibilities that come with the use of priors which, departing from typical commentaries on their scientific applications, we describe as involving reflection on how best to apply modeling tools to address matters of worldly concern.en_UK
dc.publisherFrontiers Media SAen_UK
dc.relationZyphur MJ, Hamaker EL, Tay L, Voelkle M, Preacher KJ, Zhang Z, Allison PD, Pierides DC, Koval P & Diener EF (2021) From Data to Causes III: Bayesian Priors for General Cross-Lagged Panel Models (GCLM). Frontiers in Psychology, 12, Art. No.: 612251.
dc.rights© 2021 Zyphur, Hamaker, Tay, Voelkle, Preacher, Zhang, Allison, Pierides, Koval and Diener. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY - The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.en_UK
dc.subjectpanel data modelen_UK
dc.subjectGranger causality (VAR)en_UK
dc.subjectshrinkage estimationen_UK
dc.subjectsmall-variance priorsen_UK
dc.titleFrom Data to Causes III: Bayesian Priors for General Cross-Lagged Panel Models (GCLM)en_UK
dc.typeJournal Articleen_UK
dc.citation.jtitleFrontiers in Psychologyen_UK
dc.type.statusVoR - Version of Recorden_UK
dc.contributor.affiliationUniversity of Melbourneen_UK
dc.contributor.affiliationUtrecht Universityen_UK
dc.contributor.affiliationPurdue Universityen_UK
dc.contributor.affiliationHumboldt University Berlinen_UK
dc.contributor.affiliationHumboldt University Berlinen_UK
dc.contributor.affiliationSouthern Methodist Universityen_UK
dc.contributor.affiliationUniversity of Pennsylvaniaen_UK
dc.contributor.affiliationUniversity of Melbourneen_UK
dc.contributor.affiliationUniversity of Utahen_UK
Appears in Collections:Management, Work and Organisation Journal Articles

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