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From Data to Causes III: Bayesian Priors for General Cross-Lagged Panel Models (GCLM).

Michael J Zyphur1, Ellen L Hamaker2, Louis Tay3

  • 1Department of Management and Marketing, The University of Melbourne, Parkville, VIC, Australia.

Frontiers in Psychology
|March 4, 2021
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Summary
This summary is machine-generated.

Bayesian estimation with informative priors enhances time-series and panel data models. This method improves parameter stability, prediction accuracy, and allows estimation of complex models, offering more trustworthy results than traditional maximum likelihood.

Keywords:
BayesianGranger causality (VAR)panel data modelshrinkage estimationsmall-variance priors

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Area of Science:

  • Econometrics
  • Statistical Modeling
  • Bayesian Inference

Background:

  • Traditional statistical models often struggle with parameter stability and identifiability in complex time-series and panel data.
  • Maximum Likelihood (ML) estimation can yield unreliable results for certain model specifications, particularly with time-varying parameters.

Purpose of the Study:

  • To explore the utility of Bayesian estimation with informative priors in time-series and panel data models.
  • To demonstrate how "shrinkage" or "small variance" priors, such as "Minnesota priors," can improve model performance.
  • To extend the application of Bayesian methods to the general cross-lagged panel model (GCLM).

Main Methods:

  • Utilized Bayesian estimation techniques incorporating prior probabilities alongside observed data.
  • Applied informative "shrinkage" or "small variance" priors, including "Minnesota priors."
  • Extended existing methodologies for the general cross-lagged panel model (GCLM).

Main Results:

  • Bayesian priors shrink parameter estimates, supporting an income → subjective well-being (SWB) effect not found with ML.
  • Priors enhance model parsimony, estimate stability, and out-of-sample predictive accuracy.
  • Bayesian methods enable the estimation of otherwise under-identified models, including higher-order lagged effects and time-varying parameters.

Conclusions:

  • Bayesian estimation with informative priors offers significant advantages over ML for time-series and panel data analysis.
  • These priors improve the trustworthiness and interpretability of model estimates.
  • Responsible application of Bayesian priors is crucial for addressing real-world concerns effectively.