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Quantile Regression in Lower Bound Estimation

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  • Maria Letizia Giorgetti

Abstract

In this paper, I illustrate the additional information that can be prodivided in estimating the lower bound (Sutton 1991, 1998) by using quantile regression. Quantile regression allows us to invesigate the influence of outliers. Previous lower bound have been performed using the simplex method. In this paper, the lower bound estimates are obtained using both methods for sectors belonging to a 'control group' and sectors belonging to an 'experimental group' forItalian manufacturing sectors in 1995. The data employed are drawn from the ISTAT (National Institute of Statistics, Italy) dataset. The results suggest that Sutton's predictions are robust.

Suggested Citation

  • Maria Letizia Giorgetti, 2001. "Quantile Regression in Lower Bound Estimation," STICERD - Economics of Industry Papers 29, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    • Handle: RePEc:cep:stieip:29
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    5. William Gould, 1993. "Quantile regression with bootstrapped standard errors," Stata Technical Bulletin, StataCorp LLC, vol. 2(9).
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    7. Koenker, Roger, 2000. "Galton, Edgeworth, Frisch, and prospects for quantile regression in econometrics," Journal of Econometrics, Elsevier, vol. 95(2), pages 347-374, April.
    8. Robinson, William T & Chiang, Jeongwen, 1996. "Are Sutton's Predictions Robust?: Empirical Insights into Advertising, R&D, and Concentration," Journal of Industrial Economics, Wiley Blackwell, vol. 44(4), pages 389-408, December.
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    Cited by:

    1. John Sutton, 2001. "Rich Trades, Scarce Capabilities: Industrial Development Revisited," STICERD - Economics of Industry Papers 28, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.

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