We used full-system-estimation instrumental-variables simultaneous equations modeling (IV-SEM) to examine physical activity relative to body mass index (BMI; weight (kg)/height (m)2) using 25 years of data (1985/1986 to 2010/2011) from the Coronary Artery Risk Development in Young Adults (CARDIA) Study (n = 5,115; ages 18–30 years at enrollment). Neighborhood environment and sociodemographic instruments were used to characterize physical activity, fast-food consumption, smoking, alcohol consumption, marriage, and childbearing (women) and to predict BMI using semiparametric full-information maximum likelihood estimation to control for unobserved time-invariant and time-varying residual confounding and differential measurement error through model-derived discrete random effects. Comparing robust-variance ordinary least squares, random-effects regression, fixed-effects regression, single-equation-estimation IV-SEM, and full-system-estimation IV-SEM, estimates from random- and fixed-effects models and the full-system-estimation IV-SEM were unexpectedly similar, despite the lack of control for residual confounding with the random-effects estimator. Ordinary least squares tended to overstate the significance of health behaviors in BMI, while results from single-equation-estimation IV-SEM were notably different, revealing the impact of weak instruments in standard instrumental-variable methods. Our robust findings for fixed effects (which does not require instruments but has a high cost in lost degrees of freedom) and full-system-estimation IV-SEM (vs. standard IV-SEM) demonstrate potential for a full-system-estimation IV-SEM method even with weak instruments.