The New Palgrave Dictionary of Economics

2018 Edition
| Editors: Macmillan Publishers Ltd

Estimation

  • Marc Nerlove
  • Francis X. Diebold
Reference work entry
DOI: https://doi.org/10.1057/978-1-349-95189-5_627

Abstract

Point estimation concerns making inferences about a quantity that is unknown but about which some information is available, e.g., a fixed quantity θ for which we have n imperfect measurements x1,…,xn) The theory of estimation deals with how best to use the information (combine the values x1,…,xn) to obtain a single number, estimate, for θ, say \( \widehat{\theta} \). Interval estimation does not reduce the available information to a single number and is a special case of hypothesis testing. This entry deals only with point estimation.

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Bibliography

  1. Amemiya, T. 1983. Nonlinear regression models. In Handbook of econometrics, vol. 1, ed. Z. Griliches and M.D. Intriligator. Amsterdam: North-Holland.Google Scholar
  2. Barnard, G.A., G.M. Jenkins, and C.B. Winsten. 1962. Likelihood inference and time series (with discussion). Journal of the Royal Statistical Society, Series A 125: 321–372.CrossRefGoogle Scholar
  3. Cox, D.R., and D.V. Hinkley. 1974. Theoretical statistics. London: Chapman & Hall.CrossRefGoogle Scholar
  4. Cramer, H. 1946. Mathematical methods of statistics. Princeton: Princeton University Press.Google Scholar
  5. Edwards, A.W.F. 1972. Likelihood. Cambridge: Cambridge University Press.Google Scholar
  6. Fisher, R.J. 1925. Theory of statistical estimation. Proceedings of the Cambridge Philosophical Society 22: 700–725.CrossRefGoogle Scholar
  7. Gourieroux, C., A. Monfort, and A. Trognon. 1984. Pseudo maximum likelihood methods: Theory. Econometrica 52: 681–700.CrossRefGoogle Scholar
  8. Haavelmo, T. 1944. The probability approach in econometrics. Econometrica 12(Supplement): 1–115.Google Scholar
  9. Hampel, F.R., E.M. Ronchetti, P.J. Rousseeuw, and W.A. Stahel. 1985. Robust statistics. New York: Wiley.Google Scholar
  10. Hsiao, C. 1983. Identification. In Handbook of econometrics, vol. 1, ed. Z. Griliches and M. Intriligator, 223–283. Amsterdam: North-Holland.Google Scholar
  11. Huber, P.J. 1981. Robust statistics. New York: Wiley.CrossRefGoogle Scholar
  12. Jeffreys, H. 1961. Theory of probability, 3rd ed. Oxford: Clarendon Press.Google Scholar
  13. Lehmann, E.L. 1983. The theory of point estimation. New York: Wiley.CrossRefGoogle Scholar
  14. Maritz, J.S. 1970. Empirical bayes analysis. London: Methuen.Google Scholar
  15. Neyman, J., and E.L. Scott. 1948. Consistent estimates based on partially consistent observations. Econometrica 16: 1–32.CrossRefGoogle Scholar
  16. Norden, R.H. 1972–1973. A survey of maximum-likelihood estimation. Review of the International Institute of Statistics 40: 329–354; 41: 39–58.Google Scholar
  17. Quandt, R.E. 1983. Computational problems and methods. In Handbook of econometrics, vol. 1, ed. Z. Griliches and M. Intriligator, 699–764. Amsterdam: North-Holland.Google Scholar
  18. Raiffa, H., and R. Schlaifer. 1961. Applied statistical decision theory. Boston: Harvard Business School.Google Scholar
  19. Rao, C.R. 1973. Linear statistical inference and its applications, 2nd ed. New York: Wiley.CrossRefGoogle Scholar
  20. Sargan, J.D. 1980. Identification and lack of identification. Paper presented to 4th World Congress of the Econometric Society, 28 August–2 September 1980, Aix-en-Provence.Google Scholar
  21. Savage, L.J. 1954. The foundations of statistics. New York: Wiley.Google Scholar
  22. Wald, A. 1950. Statistical decision functions. New York: Wiley.Google Scholar
  23. White, H. 1982. Maximum-likelihood estimation of misspecified models. Econometrica 50: 1–25.CrossRefGoogle Scholar
  24. Zellner, A. 1971. An introduction to Bayesian inference in econometrics. New York: Wiley.Google Scholar

Copyright information

© Macmillan Publishers Ltd. 2018

Authors and Affiliations

  • Marc Nerlove
    • 1
  • Francis X. Diebold
    • 1
  1. 1.