<abstract xmlns="http://www.w3.org/1999/xhtml"><p> The paper tackles a problem which arises during the analysis of binary models, and which is the heteroskedasticity of a random element manifested by the variable value of variance. In the paper, the following probability models, used in the analysis of a dichotomic variable, were considered: a logit model, probit model, and raybit model, which is a model proposed by the authors. The following measures of goodness of fit, present in the field literature, were considered: MSE, MAE, WMSE, and WMAE. A new measure of goodness of fit of a model was proposed, which limits the amplitude of varying values of variance.</p></abstract>

The paper tackles a problem which arises during the analysis of binary models, and which is the heteroskedasticity of a random element manifested by the variable value of variance. In the paper, the following probability models, used in the analysis of a dichotomic variable, were considered: a logit model, probit model, and raybit model, which is a model proposed by the authors. The following measures of goodness of fit, present in the field literature, were considered: MSE, MAE, WMSE, and WMAE. A new measure of goodness of fit of a model was proposed, which limits the amplitude of varying values of variance.

Goodness of Fit Measures of Models with Binary Dependent Variable which Take into Account Heteroskedasticity of a Random Element

Folia Oeconomica Stetinensia

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

The paper tackles a problem which arises during the analysis of binary models, and which is the heteroskedasticity of a random element manifested by the...

Goodness of Fit Measures of Models with Binary Dependent Variable which Take into Account Heteroskedasticity of a Random Element

Data publikacji: 04 lip 2018

Zakres stron: 182 - 194

Otrzymano: 30 paź 2017

Przyjęty: 30 mar 2018

DOI: https://doi.org/10.2478/foli-2018-0014

© 2018 Jan Purczyński, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.