GMM Estimation of a Maximum Distribution With Interval Data

Ximing Wu () and Jeffrey Perloff

Institute for Research on Labor and Employment, Working Paper Series from Institute of Industrial Relations, UC Berkeley

Abstract: We develop a GMM estimator for the distribution of a variable where summary statistics are available only for intervals of the random variable. Without individual data, once cannot calculate the weighting matrix for the GMM estimator. Instead, we propose a simulated weighting matrix based on a first-step consistent estimate. When the functional form of the underlying distribution is unknown, we estimate it using a simple yet flexible maximum entropy density. our Monte Carlo simulations show that the proposed maximum entropy density is able to approximate various distributions extremely well. The two-step GMM estimator with a simulated weighting matrix improves the efficiency of the one-step GMM considerably. We use this method to estimate the U.S. income distribution and compare these results with those based on the underlyign raw income data.

Keywords: Income; Distribution (search for similar items in EconPapers)
Date: 2005-03-01
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Citations: View citations in EconPapers (3)

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Working Paper: GMM Estimation of a Maximum Distribution With Interval Data (2005)
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