Structural Measurement Errors in Nonseparable Models
b'This paper considers measurement error from a new perspective. In surveys, response\\nerrors are often caused by the fact that respondents recall past events and quantities\\nimperfectly. We explore the consequences of recall errors for such key econometric is-\\nsues as the identification of marginal effects or economic restrictions in structural models.\\nOur identification approach is entirely nonparametric, using Matzkin-type nonseparable\\nmodels that nest a large class of potential structural models. We establish that measurement errors due to poor recall are generally likely to exhibit nonstandard behavior, in\\nparticular be nonclassical and differential, and we provide means to deal with this situation. Moreover, our findings suggest that conventional wisdom about measurement errors\\nmay be misleading in many economic applications. For instance, under certain conditions\\nleft-hand side recall errors will be problematic even in the linear model, and quantiles\\nwill be less robust than means. Finally, we apply the main concepts put forward in this\\npaper to real world data, and find evidence that underscores the importance of focusing\\non individual response behavior.'