An approximation of logarithmic functions in the regression setting |
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| Institution: | 1. Department of Economics, University of Waterloo, Waterloo, ON N2L 3G1, Canada;2. School of Business, Jianghan University, Hubei, 430056, China;3. Department of Economics, University of Connecticut, Storrs, CT 06269, USA;1. Johannes Kepler University, 4040 Linz, Austria;2. JKU Softwarepark Hagenberg, 4232 Hagenberg, Austria;3. School of Sciences, Communication University of China, Beijing 100024, China;4. Faculty of Civil Engineering, Slovak University of Technology, 813 68 Bratislava, Slovakia;5. University of Ostrava, 701 03 Ostrava, Czech Republic;6. Singidunum University, 11000 Belgrade, Serbia;7. Óbuda University, 1034 Budapest, Hungary;1. Dept. of Computer Science & Technology, Zhejiang Normal University, China;2. Dept. of Electrical & Computer Engineering, University of Massachusetts, Dartmouth, USA;3. School of Management & Economics, Beijing Institute of Technology, Beijing, China;4. School of Mechatronics, Northwestern Polytechnical University, China;1. College of Information Science & Engineering, Northeastern University, Shenyang 110004, China;2. Graduate School of Business and Law, RMIT University, Melbourne 3000, Australia;1. Southwestern University of Finance and Economics, China;2. Donghua University, China |
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| Abstract: | We consider a method of moments approach for dealing with censoring at zero for data expressed in levels when researchers would like to take logarithms. A Box–Cox transformation is employed. We explore this approach in the context of linear regression where both dependent and independent variables are censored. We contrast this method to two others, (1) dropping records of data containing censored values and (2) assuming normality for censored observations and the residuals in the model. Across the methods considered, where researchers are interested primarily in the slope parameter, estimation bias is consistently reduced using the method of moments approach. |
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| Keywords: | Box–Cox transformation Censoring Method of moments |
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