We study the relationships between national brand prices and the development of private labels, using home-scanned data from a consumer survey reporting purchases for 218 food products. In a significant number of cases (144 out of 218), we observe a positive correlation (89%) between brand price and purchases of private labels. When controlling for changes in the products quality, we still find a positive relation between private label development and national brand prices. Thus, the change in the national brand product characteristics only partly explains the increase in the national brand prices. Furthermore, the price reactions of national brands differ according to the type of private labels they are facing. Finally, we demonstrate that the development of private labels has less effect on the prices of second-tier brands than prices of the leading brand.
Individuals are commonly surveyed about their perception or assessment of risk and these variables are often used to explain individuals' actions to protect themselves against these risks. Perceptions appear as endogenous variables in traditional theoretical averting-decision models but, quite surprisingly, endogeneity of perceived risk is not always controlled for in empirical studies. In this article, we present different models that can be useful to the practitioner when estimating binary averting-decision models featuring an endogenous discrete variable (such as risk perception). In particular we compare the traditional bivariate probit model with the special regressor model, which is less well known and relies on a different set of assumptions. In the empirical illustration using household data from Australia, Canada, and France, we study how the perceived health impacts of tap water affect a household's decision to drink water from the tap. Individuals' perceptions are found to be endogenous and significant for all models, but the estimated marginal effect is sensitive to the model and underlying assumptions. The special regressor appears to be a valuable alternative to the more common bivariate probit model.
The encompassing principle has been carefully and precisely defined in various contexts, since its first appearance in the 1980s literature in numerous papers by Hendry, Mizon and Richard. Since then, several distinct notions of encompassing have been proposed and still coexist in the literature. We describe, illustrate and connect these notions in this paper. We start with the intuitive properties of exact encompassing between estimated models and compare it with its testable counterpart, approximate encompassing. We examine these notions and their main properties within static and dynamic, parametric and non-parametric, classical and Bayesian models and estimators. Encompassing or the lack of encompassing, is also studied via the concepts of parsimonious and partial encompassing. Pseudo-true values, which are central elements in measuring and testing approximate encompassing, are defined in line with the concept of specificity between models. We also examine the role played by the data generating process in the different approaches in the literature.
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