The New Palgrave Dictionary of Economics

2018 Edition
| Editors: Macmillan Publishers Ltd

Decision Theory in Econometrics

  • Keisuke Hirano
Reference work entry
DOI: https://doi.org/10.1057/978-1-349-95189-5_2297

Abstract

The decision-theoretic approach to statistics and econometrics explicitly specifies a set of models under consideration, a set of actions that can be taken, and a loss function that quantifies the value to the decision-maker of applying a particular action when a particular model holds. Decision rules, or procedures, map data into actions, and can be ordered according to their Bayes, minmax, or minmax regret risks. Large sample approximations can be used to approximate complicated decision problems with simpler ones that are easier to solve. Some examples of applications of decision theory in econometrics are discussed.

Keywords

Admissibility criterion Auction models Bayes risk Bayes rule Computational methods Decision rules Decision theory in econometrics Instrumental variables Local asymptotic normality (LAN) Markov chain Monte Carlo methods Maximum likelihood Minmax principle Minmax-regret principle Nonparametric density estimation Nonparametric models Nonparametric regression Point estimators Portfolio choice Savage, L. J. Search models Semiparametric models Statistical decision theory Time series models Treatment assignment White noise 

JEL Classifications

C44 
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Bibliography

  1. Andrews, D.W.K., Moreira, M.M. and Stock, J.H. 2004. Optimal invariant similar tests for instrumental variables regression. Discussion Paper No. 1476. Cowles Foundation, Yale University.Google Scholar
  2. Barberis, N.C. 2000. Investing for the long run when returns are predictable. Journal of Finance 55: 225–264.CrossRefGoogle Scholar
  3. Berger, J.O. 1985. Statistical decision theory and Bayesian analysis. New York: Springer.CrossRefGoogle Scholar
  4. Bickel, P.J., C.A. Klaasen, Y. Ritov, and J.A. Wellner. 1993. Efficient and adaptive estimation for semiparametric models. New York: Springer.Google Scholar
  5. Brainard, W.C. 1967. Uncertainty and the effectiveness of policy. American Economic Review 57: 411–425.Google Scholar
  6. Brock, W.A., S.N. Durlauf, and K.D. West. 2003. Policy evaluation in uncertain economic environments. Brookings Papers on Economic Activity 2003(1): 235–322.CrossRefGoogle Scholar
  7. Brown, L.D., and M.G. Low. 1996. Asymptotic equivalence of nonparametric regression and white noise. Annals of Statistics 24: 2384–2398.CrossRefGoogle Scholar
  8. Chamberlain, G. 2000. Econometric applications of maxmin expected utility. Journal of Applied Econometrics 15: 625–644.CrossRefGoogle Scholar
  9. Chamberlain, G. 2005. Decision theory applied to an instrumental variables model. Working paper, Harvard University.Google Scholar
  10. Chioda, L. and Jansson, M. 2004. Optimal conditional inference for instrumental variables regression. Working paper, UC Berkeley.Google Scholar
  11. Dehejia, R.H. 2005. Program evaluation as a decision problem. Journal of Econometrics 125: 141–173.CrossRefGoogle Scholar
  12. Ferguson, T.S. 1967. Mathematical statistics: A decision theoretic approach. New York: Academic Press.Google Scholar
  13. Giannoni, M.P. 2002. Does model uncertainty justify caution? Robust optimal monetary policy in a forward-looking model. Macroeconomic Dynamics 6(1): 111–144.CrossRefGoogle Scholar
  14. Gilboa, I., and D. Schmeidler. 1989. Maxmin expected utility with non-unique prior. Journal of Mathematical Economics 18: 141–153.CrossRefGoogle Scholar
  15. Hansen, L.P., and T.J. Sargent. 2001. Acknowledging misspecification in macroeconomic theory. Review of Economic Dynamics 4: 519–535.CrossRefGoogle Scholar
  16. Hirano, K., and J. Porter. 2003. Asymptotic efficiency in parametric structural models with parameter-dependent support. Econometrica 71: 1307–1338.CrossRefGoogle Scholar
  17. Hirano, K. and Porter, J. 2005. Asymptotics for statistical treatment rules. Working paper, University of Arizona.Google Scholar
  18. Jeganathan, P. 1995. Some aspects of asymptotic theory with applications to time series models. Econometric Theory 11: 818–887.CrossRefGoogle Scholar
  19. Kandel, S., and R.F. Stambaugh. 1996. On the predictability of stock returns: An asset-allocation perspective. Journal of Finance 51: 385–424.CrossRefGoogle Scholar
  20. Klein, R.W., and V.S. Bawa. 1976. The effect of estimation risk on optimal portfolio choice. Journal of Financial Economics 3: 215–231.CrossRefGoogle Scholar
  21. Le Cam, L. 1972. Limits of experiments. Proceedings of the Sixth Berkeley Symposium of Mathematical Statistics 1: 245–261.Google Scholar
  22. Le Cam, L. 1986. Asymptotic methods in statistical decision theory. New York: Springer.CrossRefGoogle Scholar
  23. Manski, C.F. 2004. Statistical treatment rules for heterogeneous populations. Econometrica 72: 1221–1246.CrossRefGoogle Scholar
  24. Nussbaum, M. 1996. Asymptotic equivalence of density estimation and Gaussian white noise. Annals of Statistics 24: 2399–2430.CrossRefGoogle Scholar
  25. Onatski, A., and J.H. Stock. 2002. Robust monetary policy under model uncertainty in a small model of the U.S. economy. Macroeconomic Dynamics 6(1): 85–110.CrossRefGoogle Scholar
  26. Ploberger, W. 2004. A complete class of tests when the likelihood is locally asymptotically quadratic. Journal of Econometrics 118: 67–94.CrossRefGoogle Scholar
  27. Rudebusch, G.D. 2001. Is the fed too timid? Monetary policy in an uncertain world. Review of Economics and Statistics 83: 203–217.CrossRefGoogle Scholar
  28. Savage, L.J. 1951. The theory of statistical decision. Journal of the American Statistical Association 46: 55–67.CrossRefGoogle Scholar
  29. Savage, L.J. 1954. The foundations of statistics. New York: Wiley.Google Scholar
  30. van der Vaart, A.W. 1991. An asymptotic representation theorem. International Statistical Review 59: 97–121.CrossRefGoogle Scholar
  31. Wald, A. 1939. Contributions to the theory of statistical estimation and testing hypotheses. Annals of Mathematical Statistics 10: 299–326.CrossRefGoogle Scholar
  32. Wald, A. 1950. Statistical decision functions. New York: Wiley.Google Scholar

Copyright information

© Macmillan Publishers Ltd. 2018

Authors and Affiliations

  • Keisuke Hirano
    • 1
  1. 1.