Assoc. Prof. Dr. Juan F. Muñoz

Committee: International Scientific Committee of Economics and Management Engineering
University: University of Granada
Department: Quantitative Methods in Economic and Business
Research Fields: poverty line, risk of poverty, auxiliary variable, ratio method,

Publications

3 A New Method to Estimate the Low Income Proportion: Monte Carlo Simulations

Authors: Juan F. Muñoz, Encarnación Álvarez, Rosa M. García-Fernández

Abstract:

Estimation of a proportion has many applications in economics and social studies. A common application is the estimation of the low income proportion, which gives the proportion of people classified as poor into a population. In this paper, we present this poverty indicator and propose to use the logistic regression estimator for the problem of estimating the low income proportion. Various sampling designs are presented. Assuming a real data set obtained from the European Survey on Income and Living Conditions, Monte Carlo simulation studies are carried out to analyze the empirical performance of the logistic regression estimator under the various sampling designs considered in this paper. Results derived from Monte Carlo simulation studies indicate that the logistic regression estimator can be more accurate than the customary estimator under the various sampling designs considered in this paper. The stratified sampling design can also provide more accurate results.

Keywords:

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1626
2 The Effect of Outliers on the Economic and Social Survey on Income and Living Conditions

Authors: Juan F. Muñoz, Encarnación Álvarez, Rosa M. García-Fernández, Francisco J. Blanco-Encomienda

Abstract:

The European Union Survey on Income and Living Conditions (EU-SILC) is a popular survey which provides information on income, poverty, social exclusion and living conditions of households and individuals in the European Union. The EU-SILC contains variables which may contain outliers. The presence of outliers can have an impact on the measures and indicators used by the EU-SILC. In this paper, we used data sets from various countries to analyze the presence of outliers. In addition, we obtain some indicators after removing these outliers, and a comparison between both situations can be observed. Finally, some conclusions are obtained.

Keywords: poverty line, risk of poverty, skewness coefficient, headcount index

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2095
1 On Estimating the Headcount Index by Using the Logistic Regression Estimator

Authors: Juan F. Muñoz, Encarnación Álvarez, Rosa M. García-Fernández, Francisco J. Blanco-Encomienda

Abstract:

The problem of estimating a proportion has important applications in the field of economics, and in general, in many areas such as social sciences. A common application in economics is the estimation of the headcount index. In this paper, we define the general headcount index as a proportion. Furthermore, we introduce a new quantitative method for estimating the headcount index. In particular, we suggest to use the logistic regression estimator for the problem of estimating the headcount index. Assuming a real data set, results derived from Monte Carlo simulation studies indicate that the logistic regression estimator can be more accurate than the traditional estimator of the headcount index.

Keywords: poverty line, poor, risk of poverty, sample, Monte Carlo simulations

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1623

Abstracts

4 A Comparison between Empirical and Theoretical OC Curves Related to Acceptance Sampling for Attributes

Authors: Juan F. Muñoz, Encarnación Álvarez, Francisco J. Blanco-Encomienda, Noemı Hidalgo-Rebollo

Abstract:

Many companies use the technique named as acceptance sampling which consists on the inspection and decision making regarding products. According to the results derived from this method, the company takes the decision of acceptance or rejection of a product. The acceptance sampling can be applied to the technology management, since the acceptance sampling can be seen as a tool to improve the design planning, operation and control of technological products. The theoretical operating characteristic (OC) curves are widely used when dealing with acceptance sampling. In this paper, we carry out Monte Carlo simulation studies to compare numerically the empirical OC curves derived from the empirical results to the customary theoretical OC curves. We analyze various possible scenarios in such a way that the differences between the empirical and theoretical curves can be observed under different situations.

Keywords: Quality Control, Monte Carlo Simulation, single-sampling plan, lot

Procedia PDF Downloads 303
3 The Effect of Outliers on the Economic and Social Survey on Income and Living Conditions

Authors: Juan F. Muñoz, Encarnación Álvarez, Rosa M. García-Fernández, Francisco J. Blanco-Encomienda

Abstract:

The European Union Survey on Income and Living Conditions (EU-SILC) is a popular survey which provides information on income, poverty, social exclusion and living conditions of households and individuals in the European Union. The EUSILC contains variables which may contain outliers. The presence of outliers can have an impact on the measures and indicators used by the EU-SILC. In this paper, we used data sets from various countries to analyze the presence of outliers. In addition, we obtain some indicators after removing these outliers, and a comparison between both situations can be observed. Finally, some conclusions are obtained.

Keywords: poverty line, risk of poverty, skewness coefficient, headcount index

Procedia PDF Downloads 258
2 A New Method to Estimate the Low Income Proportion: Monte Carlo Simulations

Authors: Juan F. Muñoz, Encarnación Álvarez, Rosa M. García-Fernández

Abstract:

Estimation of a proportion has many applications in economics and social studies. A common application is the estimation of the low income proportion, which gives the proportion of people classified as poor into a population. In this paper, we present this poverty indicator and propose to use the logistic regression estimator for the problem of estimating the low income proportion. Various sampling designs are presented. Assuming a real data set obtained from the European Survey on Income and Living Conditions, Monte Carlo simulation studies are carried out to analyze the empirical performance of the logistic regression estimator under the various sampling designs considered in this paper. Results derived from Monte Carlo simulation studies indicate that the logistic regression estimator can be more accurate than the customary estimator under the various sampling designs considered in this paper. The stratified sampling design can also provide more accurate results.

Keywords: auxiliary variable, poverty line, risk of poverty, ratio method

Procedia PDF Downloads 253
1 On Estimating the Headcount Index by Using the Logistic Regression Estimator

Authors: Juan F. Muñoz, Encarnación Álvarez, Rosa M. García-Fernández, Francisco J. Blanco-Encomienda

Abstract:

The problem of estimating a proportion has important applications in the field of economics, and in general, in many areas such as social sciences. A common application in economics is the estimation of the headcount index. In this paper, we define the general headcount index as a proportion. Furthermore, we introduce a new quantitative method for estimating the headcount index. In particular, we suggest to use the logistic regression estimator for the problem of estimating the headcount index. Assuming a real data set, results derived from Monte Carlo simulation studies indicate that the logistic regression estimator can be more accurate than the traditional estimator of the headcount index.

Keywords: Monte Carlo simulations, poverty line, poor, risk of poverty, sample

Procedia PDF Downloads 269