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

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.