Published

2013-01-01

Comparing TL-Moments, L-Moments and Conventional Moments of Dagum Distribution by Simulated data

Comparación de momentos TL, momentos L y momentos convencionales de la distribución Dagum mediante datos simulados

Keywords:

Dagum distribution, L-moments, Method of moments, Parameter estimation, TL-moments (en)
Distribución Dagum, estimacón de parámetros, momentos TL, momentos L, método de momentos (es)

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Authors

  • Mirza Naveed Shahzad University of Gujrat
  • Zahid Asghar Quaid-i-Azam University
Modeling income, wage, wealth, expenditure and various other social variables have always been an issue of great concern. The Dagum distribution is considered quite handy to model such type of variables. Our focus in this study is to derive the L-moments and TL-moments of this distribution in closed form. Using L & TL-moments estimators we estimate the scale parameter which represents the inequality of the income distribution from the mean income. Comparing L-moments, TL-moments and conventional moments, we observe that the TL-moment estimator has lessbias and root mean square errors than those of L and conventional estimators considered in this study. We also find that the TL-moments have smaller root mean square errors for the coefficients of variation, skewness and kurtosis. These results hold for all sample sizes we have considered in our Monte Carlo simulation study.

La modelación de ingresos, salarios, riqueza, gastos y muchas otras variables de tipo social han sido siempre un tema de gran interés. La distribución Dagum es considerada para modelar este tipo de variables. Nos centraremos en este artículo en la derivación de los momentos L y los momentos TL de esta distribución de manera cerrada. Mediante el uso de los estimadores de momentos L y TL, estimamos el parámetro de escala que representa la desigualdad de la distribución de ingresos a partir de la media. Comparando los momentos L, los momentos TL y los momentos convencionales, concluimos que los momentos TL tienen menor sesgo y errores cuadráticos medios. También concluimos que los momentos TL tiene la menor error cuadrático medio para los coeficientes de variación, sesgo y curtosis. Estas conclusiones son igualmente aplicables para todos los tamaños de muestras considerados en nuestro estudio de simulación de Monte Carlo.

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