Computational Methods for Measuring the Difference of Empirical Distributions

This paper presents a simple computational method for measuring the difference of independent empirical distributions estimated by bootstrapping or other resampling approaches. Using data from a field test of external scope in contingent valuation, this complete combinatorial method is compared with...

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Bibliographic Details
Published in:American journal of agricultural economics 2005-05, Vol.87 (2), p.353-365
Main Authors: Poe, Gregory L., Giraud, Kelly L., Loomis, John B.
Format: Article
Language:English
Subjects:
Agricultural economics
Agricultural management
Applied economics
Bootstrap mechanism
Bootstrap method
bootstrapping
C150
Combinatorial analysis
Computation
Computer programs
Confidence interval
Confidence intervals
contingent valuation
Data analysis
Economic models
Economic theory
Economics
Empirical analysis
Empirical research
Endangered species
Estimation methods
Land economics
measuring differences of distributions
Methodology
Nonparametric models
Polynomials
Programming
Q510
Q570
Samples
Sampling
Scientific method
scope testing
Significance level
Social science research
Statistical analysis
Statistical models
Studies
Valuation
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Summary:This paper presents a simple computational method for measuring the difference of independent empirical distributions estimated by bootstrapping or other resampling approaches. Using data from a field test of external scope in contingent valuation, this complete combinatorial method is compared with other methods (empirical convolutions, repeated sampling, normality, nonoverlapping confidence intervals) that have been suggested in the literature. Tradeoffs between methods are discussed in terms of programming complexity, time and computer resources required, bias, and the precision of the estimate.
ISSN:0002-9092
1467-8276
DOI:10.1111/j.1467-8276.2005.00727.x