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Our aim is to identify and estimate the distribution of the coefficients nonparametrically. We propose a kernel-based estimator for the joint probability density of the coefficients. Although this estimator shares certain features with standard nonparametric kernel density estimators, it also differs in some important characteristics that are due to the very different setup we are considering. Most importantly, the kernel is nonstandard and derives from the theory of Radon transforms. Consequently, we call our estimator the Radon transform estimator (RTE). We establish the large sample behavior of this estimator\u2014in particular, rate optimality and asymptotic distribution. In addition, we extend the basic model to cover extensions, including endogenous regressors and additional controls. Finally, we analyze the properties of the estimator in finite samples by a simulation study, as well as an application to consumer demand using British household data.<\/jats:p>","DOI":"10.1017\/s0266466609990119","type":"journal-article","created":{"date-parts":[[2009,10,26]],"date-time":"2009-10-26T11:47:47Z","timestamp":1256557667000},"page":"804-837","source":"Crossref","is-referenced-by-count":56,"title":["ANALYZING THE RANDOM COEFFICIENT MODEL NONPARAMETRICALLY"],"prefix":"10.1017","volume":"26","author":[{"given":"Stefan","family":"Hoderlein","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jussi","family":"Klemel\u00e4","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Enno","family":"Mammen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"56","published-online":{"date-parts":[[2009,10,26]]},"reference":[{"key":"S0266466609990119_ref23","doi-asserted-by":"publisher","DOI":"10.1111\/j.1468-0262.2007.00801.x"},{"key":"S0266466609990119_ref8","doi-asserted-by":"crossref","unstructured":"Connor G. , Hagmann M. , & Linton O.B. 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