10 Pages Posted: 10 May 2013
Date Written: May 8, 2013
Information on observable economic and financial variables is sometimes limited to summary form. Therefore, in many practical situations, it is desirable to restrict the flexibility of nonparametric density estimators to accommodate information about the summary data as well as any prior information about the nature of the problem. Our nonparametric estimator is easy to implement and has an explicit algebraic structure. The motivation for this letter is the generation of non-parametric densities from sparse summary data rather than from individual observations.
Keywords: nonparametric density, smoothing penalties, constraint-based
Suggested Citation: Suggested Citation
Eilers, Paul H. C. and Voudouris, Vlasios and Rigby, Robert and Stasinopoulos, Dimitrios, Estimation of Nonparametric Density from Sparse Summary Information: Densities of Undiscovered Oil (May 8, 2013). USAEE Working Paper No. 13-124. Available at SSRN: https://ssrn.com/abstract=2262584 or http://dx.doi.org/10.2139/ssrn.2262584