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Modelling cross-sectional profitability and capital intensity using panel corrected significance tests

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  • Jason Hecht

Abstract

Employing seemingly unrelated regression (SUR) models with panel corrected standard errors (PCSE) this research augments and extends Fama and French's (2000) 'first stage' model of expected cross-sectional profitability. Capital intensity, defined as the ratio of depreciation plus interest expense to total assets was found to be significantly inversely related to profitability. In addition, specific market sector and country fixed-effects proved significant in models that simultaneously corrected for cross-sectional heteroscedasticity and cross-equation residual correlation. Both of these corrections addressed the potential bias from least squares standard errors or 'inference problem' noted in the previous work by Fama and French. Unrestricted and restricted SUR cross-sectional models with PCSE are used to compute t-statistics based on Fama-MacBeth, Litzenberger-Ramaswamy and standard panel methodologies. The former two methods provided significant results compared to those using the Fama-MacBeth approach.

Suggested Citation

  • Jason Hecht, 2008. "Modelling cross-sectional profitability and capital intensity using panel corrected significance tests," Applied Financial Economics, Taylor & Francis Journals, vol. 18(18), pages 1501-1513.
    • Handle: RePEc:taf:apfiec:v:18:y:2008:i:18:p:1501-1513
    DOI: 10.1080/09603100701735938
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    1. John B. Carlson, 2001. "Why is the dividend yield so low?," Economic Commentary, Federal Reserve Bank of Cleveland, issue Apr.
    2. Asgharian, Hossein & Hansson, Björn, 2002. "Cross Sectional Analysis of the Swedish Stock Market," Working Papers 2002:19, Lund University, Department of Economics.
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    Cited by:

    1. Donghong Wu & Yiren Chen, 2023. "Digital Inclusive Finance Development and Labor Productivity: Based on a Capital-Deepening Perspective," Sustainability, MDPI, vol. 15(12), pages 1-17, June.
    2. Shetewy, Nsreen & Shahin, Ahmed Ismail & Omri, Anis & Dai, Kuizao, 2022. "Impact of financial development and internet use on export growth: New evidence from machine learning models," Research in International Business and Finance, Elsevier, vol. 61(C).

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