Regression analyses for the effect of an environmental agent on lung function often give discordant results when derived from cross-sectional compared with longitudinal studies. To evaluate why this occurs, a normal population was created by computer, and modeled to simulate functional change during life. Thus, factors known to influence lung function measurement (including those that may cause COPD) were manipulated experimentally so that their contributions to any discordance could be assessed. Regression analyses showed that significant discordance could be induced if the oldest birth cohort failed to reach the same maximal level of function as the youngest (a "cohort effect"). This distorted the cross-sectional (but not longitudinal) estimate for the dominating effect of age and additionally influenced cross-sectional estimates for the effects of partially collinear variables such as cumulative exposure to hazardous environmental dust. Discordance also occurred if regression coefficients became imprecise through random measurement/reporting error, between-subject variability, and differing susceptibility, but then the differences (sometimes marked) between cross-sectional and longitudinal estimates were not significant. We conclude that modeling a population with known characteristics can provide a useful means of demonstrating that cross-sectional versus longitudinal discordance may be fundamental and unavoidable (though explicable), or merely a consequence of imprecision.