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Journal of Economics, Finance and Administrative Science

Print version ISSN 2077-1886


SHAWTARI, Fekri Ali et al. Corporate governance characteristics and valuation: Inferences from quantile regression. Journal of Economics, Finance and Administrative Science [online]. 2016, vol.21, n.41, pp.81-88. ISSN 2077-1886.

Prior literature on corporate governance and performance provides mixed evidence on the impact of various corporate governance measures on performance indicators. However, most of literatures adopt the Ordinary Least Square (OLS). This method is based on the central tendency, which may not appropriately represent the reality in cases where the dependent variable ranges between upper and lower values and hence the relationship may not be homogenous across different percentiles of the dependent variables. A variable having a positive impact based on the central tendency for firms may not be the case for the firms in the upper or lower bounds. Thus, estimating the means using OLS may not reflect and represent the heterogeneity in the estimated relationship. Therefore, quantile regression estimates the relationship at any point conditional on the distribution of dependent variable. This would enable us to generate various estimated coefficient at certain quantile of dependent variable. Therefore, the objective of the study is twofold. First, this study aims to investigate the relationship between corporate governance and performance using OLS. Second, this work further explores the impact of corporate governance mechanisms on performance using quantile regression so as to compare and to shed light on whether there is heterogeneity in the influence of these variables on the performance of listed companies across quantiles. The results of the study provide evidence that quantile approach shows inconsistency in the result with OLS and hence indicating the impact depends on the scale size. This theoretically provides further support that OLS may represent a poor estimation approach for the reality of firms

Keywords : performance; corporate governance; OLS; quantile regression.

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