[ARTICLE] This paper introduces a general cross-lagged panel model (GCLM) for causal inference with longitudinal panel data within a structural equation modeling framework.

Co-authored by Ali Shamsollahi (ESSEC Business School), Michael J. Zyphur, Paul D. Allison, Louis Tay et al.

This is the first paper in a series of two that synthesizes, compares, and extends methods for causal inference with longitudinal panel data in a structural equation modeling (SEM) framework. Starting with a cross-lagged approach, this paper builds a general cross-lagged panel model (GCLM) with parameters to account for stable factors while increasing the range of dynamic processes that can be modeled. We illustrate the GCLM by examining the relationship between national income and subjective well-being (SWB), showing how to examine hypotheses about short-run (via Granger-Sims tests) versus long-run effects (via impulse responses). When controlling for stable factors, we find no short-run or long-run effects among these variables, showing national SWB to be relatively stable, whereas income is less so. Our second paper addresses the differences between the GCLM and other methods.

[Please read the research paper here]