Trends in Income Distribution in Italy: A Non-Parametric 90 and a Semi-Parametric Analysis

2006 ◽  
pp. 90
Author(s):  
Maria Grazia Pittau ◽  
Roberto Zelli
Keyword(s):  
Parametric Analysis ◽  
Compositional Data ◽  
Good Reason ◽  
Kernel Density ◽  
Finite Mixture ◽  
Compositional Data Analysis ◽  
Parametric Test ◽  
Mixture Density ◽  
Income Distributions ◽  
Non Parametric

Using kernel density estimation and mixture models, household size-adjusted income distributions in Italy are cross-sectionally examined over the period 1987-2002. A non-parametric test is used to assess the number of modes in the distributions. Evidence shows that income tends to cluster around more than one point. This gives good reason to model the shapes by a finite mixture density with an appropriate choice of components which represent homogeneous sub-populations. Effects of social and demographic factors on the probability of households to belong to one of the components of the mixture are identified by a compositional data analysis.

scholarly journals Explaining Disparities in AH Stock Premium Returns with Chinese Economic Policy Uncertainty: Evidence Based on a Non-Parametric Analysis

2019 ◽  
Vol 23 (4) ◽  
pp. 658-666 ◽  
Author(s):  
Mohib Ur Rahman ◽  
Bing Xu ◽  
Yuan Xiao ◽  
Wen-Tsung Lai ◽  
◽  
...  
Keyword(s):  
Economic Policy ◽  
Parameter Uncertainty ◽  
Parametric Analysis ◽  
Kernel Density ◽  
Evidence Based ◽  
Policy Uncertainty ◽  
Economic Policy Uncertainty ◽  
Premium Puzzle ◽  
The Impact ◽  
Non Parametric

This paper empirically investigates the AH share premium puzzle considering the impact of economic policy uncertainty (EPU) in China, using Hang Seng AH premium (HSAHP) index data from March 2011 to June 2018. Specifically, the index of Baker, Bloom, and Davis is used as a proxy for EPU in China. The data has been divided into two periods: 0 and 1. Period 0 includes data before the launch of the stock connect program and spans from March 2011 to October 2014, while period 1 represents data from after the launch of stock connect program and spans from November 2014 to June 2018. To more robustly test the change in AH premiums after the “connect” is launched; we evaluate the impact of Chinese EPU using non-parametric kernel density estimation. The empirical results show that parameter uncertainty explains variations in price disparity and can significantly reduce the returns of the AH share premium index.

Evaluating the Performance of Hedge Fund Strategies: A Non-Parametric Analysis

2015 ◽  
Author(s):  
Alessandra Canepa ◽  
Maria O. Gonzalez ◽  
Frank S. Skinner
Keyword(s):  
Parametric Analysis ◽  
Hedge Fund ◽  
Non Parametric

scholarly journals Testing for equality of distributions using the concept of (niche) overlap

2021 ◽  
Author(s):  
Judith H. Parkinson-Schwarz ◽  
Arne C. Bathke
Keyword(s):  
Basic Assumption ◽  
Type I Error ◽  
Test Procedure ◽  
Niche Overlap ◽  
Relative Effect ◽  
Type I ◽  
Underlying Distribution ◽  
Parametric Test ◽  
Higher Power ◽  
Non Parametric

AbstractIn this paper, we propose a new non-parametric test for equality of distributions. The test is based on the recently introduced measure of (niche) overlap and its rank-based estimator. As the estimator makes only one basic assumption on the underlying distribution, namely continuity, the test is universal applicable in contrast to many tests that are restricted to only specific scenarios. By construction, the new test is capable of detecting differences in location and scale. It thus complements the large class of rank-based tests that are constructed based on the non-parametric relative effect. In simulations this new test procedure obtained higher power and lower type I error compared to two common tests in several settings. The new procedure shows overall good performance. Together with its simplicity, this test can be used broadly.

scholarly journals Effects of Two Randomized and Controlled Multi-Component Interventions Focusing On 24-Hour Movement Behavior among Office Workers: A Compositional Data Analysis

2021 ◽  
Vol 18 (8) ◽  
pp. 4191
Author(s):  
Lisa-Marie Larisch ◽  
Emil Bojsen-Møller ◽  
Carla F. J. Nooijen ◽  
Victoria Blom ◽  
Maria Ekblom ◽  
...  
Keyword(s):  
Physical Activity ◽  
Data Analysis ◽  
Cardiorespiratory Fitness ◽  
Leisure Time ◽  
Compositional Data ◽  
Office Workers ◽  
Movement Behavior ◽  
Compositional Data Analysis ◽  
Intervention Effects ◽  
Time In Bed

Intervention studies aiming at changing movement behavior have usually not accounted for the compositional nature of time-use data. Compositional data analysis (CoDA) has been suggested as a useful strategy for analyzing such data. The aim of this study was to examine the effects of two multi-component interventions on 24-h movement behavior (using CoDA) and on cardiorespiratory fitness among office workers; one focusing on reducing sedentariness and the other on increasing physical activity. Office workers (n = 263) were cluster randomized into one of two 6-month intervention groups, or a control group. Time spent in sedentary behavior, light-intensity, moderate and vigorous physical activity, and time in bed were assessed using accelerometers and diaries, both for 24 h in total, and for work and leisure time separately. Cardiorespiratory fitness was estimated using a sub-maximal cycle ergometer test. Intervention effects were analyzed using linear mixed models. No intervention effects were found, either for 24-h behaviors in total, or for work and leisure time behaviors separately. Cardiorespiratory fitness did not change significantly. Despite a thorough analysis of 24-h behaviors using CoDA, no intervention effects were found, neither for behaviors in total, nor for work and leisure time behaviors separately. Cardiorespiratory fitness did not change significantly. Although the design of the multi-component interventions was based on theoretical frameworks, and included cognitive behavioral therapy counselling, which has been proven effective in other populations, issues related to implementation of and compliance with some intervention components may have led to the observed lack of intervention effect.

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