In this paper we provide new evidence on the impact on the U.S. CPI of the appearance and growth of new types of product outlets. Our CPI food microdata permit a more detailed categorization of outlet types than in previous studies, and we can adjust for numerous differences in item characteristics. We also examine the effects of changes in outlet mix not only across outlet categories but also within those categories. In our sample, we find that the upward impact on price from increased item quality has offset most, but not all, of the downward impact of lower-priced outlets.

We adapt techniques from the literature on chaos and nonlinear dynamics to detect misspecification in models of serially independent data by checking for dependence between the regressors and disturbances. Our tests are nonparametric in that they determine whether the distribution of the disturbances depends on the regressors without identifying a model of dependence or the distribution of the disturbances. In Monte Carlo simulations we find that these tests have good power against dependence caused by omitted variables, incorrect functional form, heteroskedasticity, and similar problems.We also apply our tests to detect misspecification in models of income imputation.