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What to Do When Your Hessian is Not Invertible

Author

Listed:
  • Jeff Gill

    (University of California, Davis)

  • Gary King

    (Harvard University)

Abstract

What should a researcher do when statistical analysis software terminates before completion with a message that the Hessian is not invertible? The standard textbook advice is to respecify the model, but this is another way of saying that the researcher should change the question being asked. Obviously, however, computer programs should not be in the business of deciding what questions are worthy of study. Although noninvertable Hessians are sometimes signals of poorly posed questions, nonsensical models, or inappropriate estimators, they also frequently occur when information about the quantities of interest exists in the data through the likelihood function. The authors explain the problem in some detail and lay out two preliminary proposals for ways of dealing with noninvertable Hessians without changing the question asked.

Suggested Citation

  • Jeff Gill & Gary King, 2004. "What to Do When Your Hessian is Not Invertible," Sociological Methods & Research, , vol. 33(1), pages 54-87, August.
  • Handle: RePEc:sae:somere:v:33:y:2004:i:1:p:54-87
    DOI: 10.1177/0049124103262681
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    References listed on IDEAS

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    1. Leamer, Edward E, 1973. "Multicollinearity: A Bayesian Interpretation," The Review of Economics and Statistics, MIT Press, vol. 55(3), pages 371-380, August.
    2. Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119.
    3. Berger, James, 1976. "Minimax estimation of a multivariate normal mean under arbitrary quadratic loss," Journal of Multivariate Analysis, Elsevier, vol. 6(2), pages 256-264, June.
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