The New Palgrave Dictionary of Economics

2018 Edition
| Editors: Macmillan Publishers Ltd

Experiments and Econometrics

  • Daniel E. Houser
Reference work entry
DOI: https://doi.org/10.1057/978-1-349-95189-5_2347

Abstract

‘Experimetrics’ refers to formal procedures used in designed investigations of economic hypotheses. Fundamental experimetric contributions by Ronald A. Fisher provided the foundation for a rich literature informing the design and analysis of economics experiments. Key components of this foundation include the concepts of randomization, independence and blocking. Experimetric analysis plays a central role in advancing economic models, and will gain further importance as scholars adopt increasingly sophisticated designed research programmes to illuminate positive economic theory.

Keywords

Blocking Causal inference Exact Test (R. A. Fisher) Experimental economics Experiments and econometrics Experimetrics Fisher, R. A. Individual learning in games Neuroeconomics Quantal response equilibrium Randomization 
This is a preview of subscription content, log in to check access

Bibliography

  1. Ashley, R., S. Ball, and C. Eckel. 2005. Motives for giving. A reanalysis of two classic public goods experiments. Manuscript, Virginia Institute of Technology.Google Scholar
  2. Bicchieri, C., and E. Xiao. 2007. Do the right thing: But only if others do. Manuscript, University of Pennsylvania.Google Scholar
  3. Box, J.F. 1980. R.A. Fisher and the design of experiments, 1922–1926. American Statistician 34: 1–7.Google Scholar
  4. El-Gamal, M.A., and D.M. Grether. 1995. Are people Bayesian? Uncovering behavioral strategies. Journal of the American Statistical Association 90: 1137–1145.CrossRefGoogle Scholar
  5. Feltovich, N. 2000. Reinforcement-based vs. belief-based learning models in experimental asymmetric-information games. Econometrica 68: 605–641.CrossRefGoogle Scholar
  6. Fisher, R.A. 1926. The arrangement of field experiments. Journal of the Ministry of Agriculture of Great Britain 33: 503–513.Google Scholar
  7. Fisher, R.A. 1935. Design of experiments. Edinburgh: Oliver and Boyd.Google Scholar
  8. Frechette, G. 2005. Session effects in the laboratory. Manuscript, New York University.Google Scholar
  9. Haile, P.A., A. Hortacsu, and G. Kosenok. 2006. On the empirical content of quantal response equilibrium. Mimeo, Yale University.Google Scholar
  10. Houser, D., M. Keane, and K. McCabe. 2004. Behavior in a dynamic decision problem: An analysis of experimental evidence using a Bayesian type classification algorithm. Econometrica 72: 781–822.CrossRefGoogle Scholar
  11. Jonckheere, A.R. 1954. A distribution-free k-sample test against ordered alternatives. Biometrika 41: 133–145.CrossRefGoogle Scholar
  12. Loomes, G. 2005. Modelling the stochastic component of behavior in experiments: Some issues for the interpretation of data. Experimental Economics 8: 301–323.CrossRefGoogle Scholar
  13. McKelvey, R.D., and T.R. Palfrey. 1995. Quantal response equilibria for normal form games. Games and Economic Behavior 10: 6–38.CrossRefGoogle Scholar
  14. Salmon, T.C. 2001. An evaluation of econometric models of adaptive learning. Econometrica 69: 1597–1628.CrossRefGoogle Scholar
  15. Siegel, S., and N. Castellan Jr. 1988. Nonparametric statistics for the behavioral sciences. 2nd ed. Boston: McGraw Hill.Google Scholar
  16. Wilcox, N. 2006. Theories of learning in games and heterogeneity bias. Econometrica 74: 1271–1292.CrossRefGoogle Scholar

Copyright information

© Macmillan Publishers Ltd. 2018

Authors and Affiliations

  • Daniel E. Houser
    • 1
  1. 1.