A quantile regression analysis on corporate governance and the cost of bank loans: a research note. Issue 1 (9th February 2015)

• Record Type: Journal Article
• Authors: Chi, Wuchun; Huang, Huichi; Xie, Hong
• Is Part Of: Review of accounting and finance. Volume 14:Issue 1(2015)
• Page Start: 2
• Page End: 19
• Publication Date: 2015-02-09
• Subjects: Corporate governance -- Board characteristics -- Cost of debt -- Least absolute deviations -- Median regression -- Quantile regression; Accounting -- Periodicals; Corporations -- Finance -- Periodicals; 657.05
• DOI: 10.1108/RAF-12-2012-0126
• Languages: English
• ISSNs: 1475-7702

Abstract

Abstract : Purpose: – This paper aims to investigate whether there is heterogeneity in the relationship between the bank loan interest rate and its determinants using the quantile regression method and to reconcile some conflicting findings in prior literature. Design/methodology/approach: – First, the effects of 18 determinants were examined on the bank loan interest rate using the ordinary least squares method (OLS). Second, it was investigated whether the relationship between the loan rate and its determinants is heterogeneous across quantiles of loan rates using the quantile regression method. Findings: – Considerable heterogeneity was found in the relationship between the loan rate and its determinants. Specifically, a determinant that is beneficial for the bank loan rate, on average, as revealed by the OLS method may become unimportant or even detrimental for firms located at extremely high or low loan rate quantiles. By revealing extreme heterogeneity in the relationship between the loan rate and some of its determinants, the authors potentially explain two conflicting findings in prior literature. Originality/value: – The conventional OLS method masks the heterogeneity in the relationship between the bank loan interest rate and its determinants. Quantile regression can be used to supplement the OLS estimates to gain a more detailed and complete picture of the relationship between the dependent variable and explanatory variables.