El problema de un tamaño muestral pequeño en la regresion linealMicronumerosidad

  1. Salmerón Gómez, Román 1
  2. Blanco Izquierdo, Víctor 1
  1. 1 Universidad de Granada
    info

    Universidad de Granada

    Granada, España

    ROR https://ror.org/04njjy449

Journal:
Rect@: Revista Electrónica de Comunicaciones y Trabajos de ASEPUMA

ISSN: 1575-605X

Year of publication: 2016

Volume: 17

Issue: 2

Pages: 167-177

Type: Article

DIALNET GOOGLE SCHOLAR lock_openDialnet editor

More publications in: Rect@: Revista Electrónica de Comunicaciones y Trabajos de ASEPUMA

Abstract

The econometrician Arthur Goldberg introduced the notion of micronumerosity motivating that classical Econometrics textbooks used to explain the problem of multicollinearity but nothing is explain about the analogous problem of estimating using an small size sample. Then, micronumerosity refers to multicollinearity because of small samples. Since its origins are very particular, its treatment should also be speci c. In this paper we obviate standard multicollinearity solutions and we propose a new scheme based on the speci c charasteristics of the problem

Bibliographic References

  • A. Spanos, A. McGuirk (2002): The problem of near-multicollinearity revisited: erratic vs systematic volatily, Journal of Econometrics. 108 (2), 365–393.
  • A. Goldberger (1991): A course in Econometrics (Cambridge, MA: Harvard University Press).
  • R.M. O’Brien (2007): A caution regarding rules of thumb for variance inflation factors, Quality and Quantity, 41, 673–690.
  • R. Salmerón, V. Blanco (2016): Micronumerosidad aproximada y regresi´on lineal mu´ltiple, XXIV Jornadas de ASEPUMA y XII Encuentro Internacional, Granada, España, 159–168.
  • D. Gujarati (2004): Basic Econometrics (McGraw-Hill, 4a Edició).
  • J.M. Wooldridge (2006): Introducci´on a la Econometr´ıa: Un Enfoque Moderno (Thomson, 2a Edición).
  • S. Nickel, J. Puerto (2005): Location Theory: A Unified Approach. Berlin, Springer.
  • V. Blanco, J. Puerto, R. Salmero´n, R. (2016): A general framework for multiple linear regression. Submitted. Preprint available at https://arxiv.org/abs/1505.03451.
  • P. Rousseeuw, A. Leroy, A. (2003): Robust Regression and Outlier Detection. New York: Wiley.
  • V. Blanco, J. Puerto, S. El-Haj Ben-Ali (2014): Revisiting several problems and algorithms in continuous location with Rτ norms. Comput. Optim. Appl. 58 (3), 563–595.
  • R.H. Myers (1990): Classical and modern regression with apllications” (2a Edici´on, PWS-Kent).
  • L.R. Klein, A.S. Goldberger (1964): An economic model of the United States, 1929-1952 (Amsterdan: North Holland Publishing Company).
Data source: Dialnet