Empirical modeling of section 162(a) (2) tax court decisions: Identifying the location of a tax home |
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| Institution: | 1. Department of Management, School of Human Resource Management, School of Business, University of Granada, Rector Lopez Argueta s/n, 18071 Granada, Spain;2. Department of Design, Faculty of Engineering Technology, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands;1. University of South Carolina, College of Hospitality, Retail & Sport Management, 1010E Carolina Coliseum, Columbia, SC 29208, USA;2. The School of Marketing and Reputation, Henley Business School, University of Reading, Reading RG6 6AU, UK;3. College of Business and Economics, University of Hawaii at Hilo, 200 West Kawili Street, Hilo, HI 96720, USA;1. Hamburg University of Technology, Am-Schwarzenberg-Campus 4 (D), 21073 Hamburg, Germany;2. University of Newcastle, University Dr, Callaghan, NSW 2308, Australia;1. University of Dayton, 300 College Park, Dayton, OH 45469, United States;2. Whitman School of Management, Syracuse University, 721 University Ave, Syracuse, NY 13244, United States |
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| Abstract: | One-hundred Tax Court cases concerning Section 162(a) (2) were subjected to several multivariate PROBIT and discriminant analyses to determine which factors best define the location of a “tax home.” In each case, the government disagreed with the taxpayer and contended that the “tax home” was either nonexistent or was located in elsewhere. Various sensitivity analyses were performed to test model specifications regarding linear or quadratic functions, discriminant or PROBIT models, and temporal stability. A seven-variable linear discriminant model achieved an 88% Lachenbruch U classification accuracy and exhibited high stability with regard to the three sensitivity analyses. Implications for taxpayers, practitioners, legislators, and researchers are discussed. |
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