Modeling Firm-Size Distribution Using Box-Cox Heteroscedastic Regression
AbstractUsing the Box-Cox regression model with heteroscedasticity, we examine the size distribution of firms. Analyzing the data set of Portuguese manufacturing firms as in Machado and Mata (2000), we show that our approach compares favorably against the Box-Cox quantile regression method. In particular, we are able to answer the key questions addressed by Machado and Mata, with the additional advantage that our empirical quantile functions are monotonic. Furthermore, confidence intervals of the regression quantiles are easy to compute, and the estimation of the Box-Cox heteroscedastic regression model is straightforward.
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Bibliographic InfoPaper provided by Singapore Management University, School of Economics in its series Working Papers with number 10-2004.
Length: 26 pages
Date of creation: Mar 2004
Date of revision:
Publication status: Published in SMU Economics and Statistics Working Paper Series
Other versions of this item:
- Y. K. Tse & Z. L. Yang, 2006. "Modelling firm-size distribution using Box-Cox heteroscedastic regression," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(5), pages 641-653.
- C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
- C5 - Mathematical and Quantitative Methods - - Econometric Modeling
- L11 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Production, Pricing, and Market Structure; Size Distribution of Firms
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Jose A. F. Machado & Jose Mata, 2000. "Box-Cox quantile regression and the distribution of firm sizes," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(3), pages 253-274.
- Moshe Buchinsky, 1998. "Recent Advances in Quantile Regression Models: A Practical Guideline for Empirical Research," Journal of Human Resources, University of Wisconsin Press, vol. 33(1), pages 88-126.
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