scholarly journals Bayesian quantile regression analysis for continuous data with a discrete component at zero

2017 ◽  
Vol 18 (1) ◽  
pp. 73-93 ◽  
Author(s):  
Bruno Santos ◽  
Heleno Bolfarine
Keyword(s):  
Regression Analysis ◽  
Quantile Regression ◽  
Durable Goods ◽  
Continuous Distribution ◽  
Real Data ◽  
Discrete Component ◽  
Bayesian Quantile Regression ◽  
Conditional Quantiles ◽  
Quantile Regression Analysis ◽  
Quantile Regression Method

In this work, we propose a Bayesian quantile regression method to response variables with mixed discrete-continuous distribution with a point mass at zero, where these observations are believed to be left censored or true zeros. We combine the information provided by the quantile regression analysis to present a more complete description of the probability of being censored given that the observed value is equal to zero, while also studying the conditional quantiles of the continuous part. We build up a Markov Chain Monte Carlo method from related models in the literature to obtain samples from the posterior distribution. We demonstrate the suitability of the model to analyse this censoring probability with a simulated example and two applications with real data. The first is a well-known dataset from the econometrics literature about women labour in Britain, and the second considers the statistical analysis of expenditures with durable goods, considering information from Brazil.

scholarly journals A study about the determinant factors of the capital structure of Brazilian companies: a quantile regression analysis

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Osvaldo Martins Quintella Junior ◽  
Claudio Ulysses Ferreira Coelho
Keyword(s):  
Regression Analysis ◽  
Capital Structure ◽  
Quantile Regression ◽  
Relevant Variables ◽  
Quantile Regression Analysis ◽  
Data Inference ◽  
Quantile Regression Method ◽  
The Relationship ◽  
The Capital Structure

Purpose: The present paper aims to analyses the determinants of capital structure of ninety-four organizations from the five hundred largest Brazilian companies according to 2018 Exame magazine yearbook. Methodology: For this research we used information from financial statements of the five hundred largest Brazilian companies.  Data inference was made through descriptive statistical analysis and quantile regression analysis. The data was obtained through the companies’ websites and through economática software. The descriptive and econometric analysis were performed using Stata 12 software. Results: The results indicate that the variables such as size and structure of the assets are significant in some quantiles to determine how companies adopt a particular capital structure. In addition, the results indicate the relationship between firm size and total debt level is positive for 25th quantile of the sample. Another point to note is that the asset structure variable has a positive relationship with the long-term debt variable for the 75th and 95th quantiles. Contributions of the study: This research is an important contribution for finance literature considering that the quantile regression method was used. The scarcity of research using this method is notorious. Moreover, the results obtained in other works on the subject are not yet convergent about the relevant variables to determine the capital structure.

scholarly journals Association between leukocyte telomere length and sex by quantile regression analysis

2021 ◽  
Author(s):  
Fernanda Gutierrez-Rodrigues ◽  
Raquel M. Alves-Paiva ◽  
Natália F. Scatena ◽  
Edson Z. Martinez ◽  
Priscila S. Scheucher ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Quantile Regression ◽  
Telomere Length ◽  
Leukocyte Telomere Length ◽  
Quantile Regression Analysis

Determinants of liquidity in Malaysian SMEs: a quantile regression approach

2018 ◽  
Vol 67 (9) ◽  
pp. 1566-1584 ◽  
Author(s):  
Shaista Wasiuzzaman
Keyword(s):  
Regression Analysis ◽  
Quantile Regression ◽  
Emerging Market ◽  
Developed Countries ◽  
Data Availability ◽  
Firm Level ◽  
Liquidity Management ◽  
Ols Regression ◽  
Content Type ◽  
Quantile Regression Analysis

PurposeThe management of liquidity has always been seen as a critical but often ignored issue in finance. Despite the abundance of studies on liquidity management, these studies mainly focus on developed countries and on large firms. Liquidity is critical for the small firm but studies on liquidity management in small and medium enterprises (SMEs) are lacking. The purpose of this paper is to examine the firm-level determinants of liquidity of SMEs in Malaysia.Design/methodology/approachData are collected for a total of 986 small firms in Malaysia from 2011 to 2014, resulting in a total of 2,683 observations. Firm-specific variables and the effect of the economy are considered as the possible determinants of liquidity. Ordinary least squares (OLS) regression analysis with standard errors adjusted for firm-level clustering and quantile regression analysis are used for this purpose.FindingsAnalysis using OLS regression technique indicates that a firm’s profitability, its growth, asset tangibility, size, age and firm status are significant factors in influencing its liquidity decision. Leverage and economic condition are not found to have any significant influence on liquidity. However, quantile regression analysis provides a different picture especially for SMEs with liquidity at the quantile levels ofθ=0.10 and 0.90. Atθ=0.10, only profitability, tangibility and firm status are significant, while atθ=0.90, tangibility, size, firm status and, to some extent, age are significant in influencing liquidity levels.Originality/valueTo the author’s knowledge, this is the first study analyzing the liquidity decision of SMEs in an emerging market such as Malaysia. Most studies on liquidity management of SMEs are focused on developed countries due to data availability but these studies are also only a handful. Additionally, this study uses quantile regression analysis which highlights the need to analyze financial decisions at different levels rather than at the aggregate level as done in OLS regression analysis.

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