Calculating a Standard Error for the Gini Coefficient: Some Further Results*

Giles, David E. A.

doi:10.1111/j.1468-0084.2004.00086.x

Cited by 81 publications

(77 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This is a very important point. Research papers that describe model performance with Gini should always include the standard error 13 …”

Section: Database Model Evaluationmentioning

confidence: 99%

“…14 -16 Moreover, the database marketing summaries do not possess the properties appropriate for computing the standard error of Gini using Giles method. 13 The remaining portion of this article is organized as follows. We discuss response model evaluation, summarize the descriptive methods used by practitioners, describe how descriptive methods are related to the Gini statistic, describe a method for approximating Gini, present a procedure for estimating the standard error of Gini and, lastly, illustrate our suggested procedure using three data sets, two available from the Direct Marketing Association (DMA) and the third a subset of a proprietary data fi le from a large, national insurance company.…”

Section: Introductionmentioning

confidence: 99%

“…When the data points represent individual data points and are sorted according to their unique values, Giles 13 describes a regression approach. There is still some controversy about the approach.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Assessing model performance: The Gini statistic and its standard error

Greene

Milne²

2010

J Database Mark Cust Strategy Manag

View full text Add to dashboard Cite

Database marketers frequently create statistical models to assess customers in terms of loyalty, lifetime value, responsiveness or acquisition. An assortment of modeling techniques, for example RFM (Recency-Frequency-Monetary value) models , regression, logit, neural networks and genetic algorithms, have been investigated to determine the most appropriate and effective technique. For the most part, traditional statistical measures utilize R 2 , the F statistic, the Chi Square statistic, various classifi cation indices and so forth to assess model performance -with an emphasis on goodness of fi t, and measuring how closely data points fi t a statistical model. Practitioners, on the other hand, typically use summarized descriptive methods to assess model performance: decile analysis, lift charts, cumulative lift charts, gains tables and cumulative gains tables. Both of these approaches have limitations. Some academic researchers have suggested that tradi tional goodness of fi t statistics are not appropriate for evaluating model performance when the objective is to create models that maximize differentiation between population segments in terms of response rates. The traditional statistical measures are appropri ate for assessing how well individual response values fi t a given model (for example minimize least square errors between response data points and predicted values); however, they are not appropriate for effectively selecting market segments or individual customers for targeting and meeting business objectives. The descriptive measures used by practitioners, although visually appealing, do not assess overall model performance with statistical certainty. This research offers a remedy for the current situation by proposing the use of the Gini statistic and the associated standard error. We explain the Gini statistic and how it is connected to commonly used assessment measures. We then describe a simple method for computing Gini and its standard error. The accuracy of the method is demonstrated with specifi c industry data fi les.

show abstract

“…This is a very important point. Research papers that describe model performance with Gini should always include the standard error 13 …”

Section: Database Model Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Assessing model performance: The Gini statistic and its standard error

Greene

Milne²

2010

J Database Mark Cust Strategy Manag

View full text Add to dashboard Cite

show abstract

“…Ogwang (2000) provided a method to compute the Gini index by an ordinary least square (OLS) regression, as well as discussed how to use the regression to simplify the computation of the jackknife SE. Giles (2004) later showed that the OLS SE from this regression could be used directly in order to compute the SE of the Gini index. Modarres and Gastwirth (2006) with simulations showed that the regression method overestimates the SE of the Gini estimator.…”

Section: Introductionmentioning

confidence: 99%

A comparative study of the Gini coefficient estimators based on the regression approach

Mirzaei

Borzadaran

Amini

et al. 2017

CSAM

View full text Add to dashboard Cite

Resampling approaches were the first techniques employed to compute a variance for the Gini coefficient; however, many authors have shown that an analysis of the Gini coefficient and its corresponding variance can be obtained from a regression model. Despite the simplicity of the regression approach method to compute a standard error for the Gini coefficient, the use of the proposed regression model has been challenging in economics. Therefore in this paper, we focus on a comparative study among the regression approach and resampling techniques. The regression method is shown to overestimate the standard error of the Gini index. The simulations show that the Gini estimator based on the modified regression model is also consistent and asymptotically normal with less divergence from normal distribution than other resampling techniques.

show abstract

“…Yao and Liu (1996) and Yao (1999) proposed convenient ways of conducting these decompositions using spreadsheets without invoking any regressions. The stochastic approach considered by Ogwang (2000Ogwang ( , 2004Ogwang ( , 2006Ogwang ( , 2007 and Giles (2004) provides a simple way of computing the Gini index from the estimated parameters of an underlying regression model with a known form of heteroscedasticity related to income. The purpose of this article is to exploit the simplification provided by the stochastic approach for purposes of conducting three-component subgroup decomposition of the traditional Gini index.…”

Section: Introductionmentioning

confidence: 99%

A Convenient Method of Decomposing the Gini Index by Population Subgroups

Ogwang¹

2014

Journal of Official Statistics

View full text Add to dashboard Cite

We propose a convenient method of estimating the within-group, between-group, and interaction components of the overall traditional Gini index from the estimated parameters of underlying "trick regression models" involving known forms of heteroscedasticity related to income. Two illustrative examples involving both real and artificial data are provided. The issue of appropriate standard error of the subgroup decomposition is also discussed.

show abstract

Calculating a Standard Error for the Gini Coefficient: Some Further Results*

Cited by 81 publications

References 26 publications

Assessing model performance: The Gini statistic and its standard error

Assessing model performance: The Gini statistic and its standard error

A comparative study of the Gini coefficient estimators based on the regression approach

A Convenient Method of Decomposing the Gini Index by Population Subgroups

Contact Info

Product

Resources

About