scholarly journals Composite Pareto Distributions for Modelling Household Income Distribution in Malaysia

2021 ◽  
Vol 50 (7) ◽  
pp. 2047-2058
Author(s):  
Muhammad Hilmi Abdul Majid ◽  
Kamarulzaman Ibrahim
Keyword(s):  
Income Distribution ◽  
Household Income ◽  
Goodness Of Fit ◽  
Threshold Value ◽  
Goodness Of Fit Test ◽  
Income Distributions ◽  
Pareto Distributions ◽  
Tail Behaviour ◽  
Kolmogorov Smirnov ◽  
Income Data

Composite Pareto distributions are flexible as the models allow for data to be described by two distributions: a Pareto distribution for the data above a threshold value and another separate distribution for data below the threshold value. It is noted in some previous literatures that the Paretian tail behaviour can be observed in the distribution of Malaysian household income. In this paper, the composite Pareto models are fitted to the Malaysian household income data of several years. These fitted composite Pareto models are then compared to several univariate models for describing income distribution using pseudo-likelihood based AIC, BIC and Kolmogorov-Smirnov goodness-of-fit test. It is found that the income distributions in Malaysia can be best described by the lognormal-Pareto (II) model as compared to other candidate models.

scholarly journals B40 Group Income Household Trend in Malaysia

2020 ◽  
Vol 9 (12) ◽  
pp. 126-129
Keyword(s):  
Household Income ◽  
Goodness Of Fit ◽  
Likelihood Estimation ◽  
Complex Model ◽  
Parameter Distribution ◽  
Average Income ◽  
Goodness Of Fit Test ◽  
Income Household ◽  
The Future ◽  
Income Data

Income inequality is crucial issue in the Malaysian economy. This issue has a great impact especially on the B40 group income household because of the rising cost of living today. Therefore, modelling of income data is done to look at income pattern of B40 group in Malaysia. Household income data for Malaysia in year 2007, 2009, 2012, 2014 and 2016 have been used in this study. The income distribution used in this study is a two-parameter distribution of Weibull, Log Normal, Fisk and Gamma. This study uses only two parametric distributions to suit the income data because the simplest model is better than the complex model. The best distribution selection is performed with the fitting of statistical distribution through maximum likelihood estimation (MLE) method. Goodness of fit test has been done to model B40 household income data. The best model for each year used to predict the average income in the future by using regression method. Weibull distribution is the best model for B40 household income data. The study also shows that the average income of the B40 group in the future will increase. Therefore, this study was conducted to assist B40 group to be more sensitive to the Malaysian economy and plan their income wisely.

scholarly journals After the Financial Crisis: The Evolution of the Global Income Distribution Between 2008 and 2013

2020 ◽  
Author(s):  
Branko Milanovic
Keyword(s):  
Financial Crisis ◽  
Income Distribution ◽  
Household Income ◽  
Global Financial Crisis ◽  
Income Growth ◽  
Global Inequality ◽  
Working Paper ◽  
Income Data ◽  
The Global Financial Crisis ◽  
China’S Growth

Using the newly created, and in terms of coverage and detail, the most complete household income data from more than 130 countries, the paper analyzes the changes in the global income distribution between 2008 and 2013. This was the period of the global financial crisis and recovery. It is shown that global inequality continued to decline, largely due to China’s growth that explains one-half of global Gini decrease between 2008 and 2013. Income growth of the global top 1 percent slowed significantly. The slowdown is present even after survey data are corrected for the likely underestimation of highest incomes. The paper ends with a discussion of the effects of the financial crisis in the light of an even more serious looming crisis caused by the 2019-20 pandemic. (Stone Center on Socio-Economic Inequality Working Paper)

scholarly journals AJUSTE DA FUNÇÃO DE DISTRIBUIÇÃO DIAMÉTRICA WEIBULL POR PLANILHA ELETRÔNICA

FLORESTA ◽  
2011 ◽  
Vol 41 (2) ◽  
Author(s):  
William Thomaz Wendling ◽  
Dartagnan Baggio Emerenciano ◽  
Roberto Tuyoshi Hosokawa
Keyword(s):  
Distribution Function ◽  
Goodness Of Fit ◽  
Diameter Distribution ◽  
Goodness Of Fit Test ◽  
Function Model ◽  
Helpful Tool ◽  
Test Efficiency ◽  
Kolmogorov Smirnov ◽  
Electronic Spreadsheet ◽  
Main Model

Desenvolve-se uma metodologia traçada por um roteiro em algoritmo factível e amigável para efetivação em planilhas eletrônicas, reconhecidas como uma interface popular para cálculos. Busca-se, assim, apresentar uma ferramenta útil para alunos de graduação e recém-graduados em engenharia florestal, ou engenheiros mais experientes que ainda não dominem a técnica, para ajuste de um modelo de função densidade de probabilidade, com o objetivo de descrever a estrutura da distribuição diamétrica de populações florestais. O modelo adotado é o da função de Weibull, e o método de ajuste é o do percentis, com simulações comparadas por teste de aderência de Kolmogorov-Smirnov. A eficiência do método apresentado é testada por comparação a outro método alternativo.Palavras-chave:  Manejo florestal; florestas - modelos matemáticos; florestas - simulação por computador. AbstractWeibull diameter distribution function adjusts for electronic spreadsheet. This research develops a methodology based on easy and friendly algorithm for spreadsheets, a well known interface for calculus. It aims to present a helpful tool for forestry students, as well as for newly or experienced engineers who haven’t already known adjustment techniques for a density function model of probability, which is useful into diametric distribution structure descriptions of forest population. It has Weibull’s function as main model, percentile as adjustment method, and comparing simulations by Kolmogorov-Smirnov goodness-of-fit test. Efficiency of the presented method was tested by comparison to another method.Keywords: Forest management; forest - mathematical models; forest - computer simulator.

Utilizing Simulation to Build a Retail Store Inventory Optimization Model

2013 ◽  
Vol 5 (1) ◽  
Author(s):  
VICENTE SALVADOR E. MONTAÑO ◽  
MICHAEL E. CARTER II
Keyword(s):  
Simulation Model ◽  
Goodness Of Fit ◽  
Inventory Model ◽  
T Test ◽  
Optimal Order ◽  
Order Quantity ◽  
Goodness Of Fit Test ◽  
Reorder Point ◽  
Kolmogorov Smirnov ◽  
Optimal Order Quantity

The researchers build an inventory model for retail stores by validating their economicorder quantity through data driven simulation. This paper created an inventoryoptimization model for a personal care retailing business, to avoid stock out and minimize their holding cost and ordering cost. Simulating a thousand different scenarios, the research come up with an optimal inventory model for the two most sellable products in the store. The t-test reveals that product A has a significantly higher demand than product B. The simulation model validates the optimal order quantity of 59 units, with a reorder point of 25 units for product A. However, the simulation model recommends an optimal order quantity of 37 units and a reorder point of 10 units for product B. The Kolmogorov-Smirnov Goodness of Fit Test reveals the normal distribution of the 30 days inventory for Product A but not for Product B. Confirming that stocks out will unlikely happen for product A but will probably occur for product B. The model confirms EOQ findings of product with relatively high demand but low price but a departure for products with low demand but the high price.Keywords: Operations management, retail inventory system, t-test, Monte Carlo Simulation,Kolmogorov-Smirnov Goodness of Fit Test, Davao City, Philippines, Southeast Asia

scholarly journals A Cautionary Note on the Use of the Kolmogorov–Smirnov Test for Normality

2007 ◽  
Vol 135 (3) ◽  
pp. 1151-1157 ◽  
Author(s):  
Dag J. Steinskog ◽  
Dag B. Tjøstheim ◽  
Nils G. Kvamstø
Keyword(s):  
Goodness Of Fit ◽  
Small Sample ◽  
Power Comparison ◽  
Goodness Of Fit Test ◽  
Kolmogorov Smirnov ◽  
The Mean ◽  
Small Sample Problem ◽  
Tests For Normality ◽  
Test For Normality ◽  
Smirnov Test

Abstract The Kolmogorov–Smirnov goodness-of-fit test is used in many applications for testing normality in climate research. This note shows that the test usually leads to systematic and drastic errors. When the mean and the standard deviation are estimated, it is much too conservative in the sense that its p values are strongly biased upward. One may think that this is a small sample problem, but it is not. There is a correction of the Kolmogorov–Smirnov test by Lilliefors, which is in fact sometimes confused with the original Kolmogorov–Smirnov test. Both the Jarque–Bera and the Shapiro–Wilk tests for normality are good alternatives to the Kolmogorov–Smirnov test. A power comparison of eight different tests has been undertaken, favoring the Jarque–Bera and the Shapiro–Wilk tests. The Jarque–Bera and the Kolmogorov–Smirnov tests are also applied to a monthly mean dataset of geopotential height at 500 hPa. The two tests give very different results and illustrate the danger of using the Kolmogorov–Smirnov test.

A guided nonparametric goodness-of-fit test with application to income distributions

2019 ◽  
Vol 22 (3) ◽  
pp. 207-222
Author(s):  
Kuangyu Wen ◽  
Ximing Wu
Keyword(s):  
Goodness Of Fit ◽  
Bias Reduction ◽  
Density Estimator ◽  
Consistent Estimate ◽  
Finite Sample ◽  
Goodness Of Fit Test ◽  
Normal Mixtures ◽  
Parametric Density ◽  
Income Distributions ◽  
Goodness Of Fit Tests

Summary We have developed a customizable goodness-of-fit test of a parametric density based on its distance to a consistently estimated density. This consistent estimate is obtained via a nonparametric density estimator with a parametric start, wherein the start is set to be the hypothesized parametric density. To cope with the influence of nonparametric estimation bias, nonparametric goodness-of-fit tests have resorted to remedies such as undersmoothing or convolution of the hypothesized density. Our test requires no such devices and possesses enhanced powers against alternative densities because the guided density estimator is free of the typical nonparametric bias under the null hypothesis and attains bias reduction when the underlying density is in a broad nonparametric neighborhood of the hypothesized density. Here, we establish the statistical properties of our test and use Monte Carlo simulations to demonstrate its finite sample performance. We use this test to examine the goodness-of-fit of normal mixtures to the distributions of log income of U.S. states. Although normality is rejected decisively, our results suggest that normal mixtures with two or three components suffice for all but one state.

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