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Diagnosing Failure: When is an Estimation Problem Too Large for a PC?

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  • B. D. McCullough and H. D. Vinod

Abstract

Sometimes numerical failure of an econometric software package is quite stark: a nonlinear procedure fails to converge; illegal arguments to a function cause an abnormal end; matrices cannot be inverted. Other times a package fails without warning, and these types of failures are particularly pernicious. One such failure in this latter class occurs when a problem simply exhausts the numerical limits of the computer, e.g., attempting to solve a problem that is larger than the computer can handle. In such situations, the user must be conscious that he is nearing the limits of the computer and test carefully to determine whether or not the problem has exhausted the computer's capabilities. Making use of the replication policy of the American Economic Review, we analyze just such a recently-published problem involving an attempt to maximize a 48 parameter nonlinear maximum likelihood problem. We show that the problem, as posed, cannot be reliably solved in double precision on a PC with a 32-bit word.

Suggested Citation

  • B. D. McCullough and H. D. Vinod, 2001. "Diagnosing Failure: When is an Estimation Problem Too Large for a PC?," Computing in Economics and Finance 2001 246, Society for Computational Economics.
    • Handle: RePEc:sce:scecf1:246
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    Cited by:

    1. Barrera, Carlos, 2014. "La relación entre los ciclos discretos en la inflación y el crecimiento: Perú 1993 - 2012," Working Papers 2014-024, Banco Central de Reserva del Perú.

    More about this item

    Keywords

    ill-conditioned Hessian; nonlinear maximum likelihood;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

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