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Circumventing the Curse of Dimensionality in Applied Work Using Computer Intensive Methods

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  • Weeks, Melvyn

Abstract

Following recent advances in the development of simulation-based inference, the author outlines a suite of programs designed to circumvent the 'curse of dimensionality' common to the class of so-called qualitative and limited dependent variable models. The author discusses the nature of the dimensionality problem, briefly introduces the form of a simple simulation technique, outlines the structure and capabilities of the programs, and provides a numerical experiment. Copyright 1995 by Royal Economic Society.

Suggested Citation

  • Weeks, Melvyn, 1995. "Circumventing the Curse of Dimensionality in Applied Work Using Computer Intensive Methods," Economic Journal, Royal Economic Society, vol. 105(429), pages 520-530, March.
    • Handle: RePEc:ecj:econjl:v:105:y:1995:i:429:p:520-30
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    Cited by:

    1. Lamperti, Francesco & Roventini, Andrea & Sani, Amir, 2018. "Agent-based model calibration using machine learning surrogates," Journal of Economic Dynamics and Control, Elsevier, vol. 90(C), pages 366-389.
    2. repec:hal:spmain:info:hdl:2441/13thfd12aa8rmplfudlgvgahff is not listed on IDEAS
    3. repec:hal:spmain:info:hdl:2441/20hflp7eqn97boh50no50tv67n is not listed on IDEAS
    4. Lamperti, Francesco & Roventini, Andrea & Sani, Amir, 2018. "Agent-based model calibration using machine learning surrogates," Journal of Economic Dynamics and Control, Elsevier, vol. 90(C), pages 366-389.
    5. Grazzini, Jakob & Richiardi, Matteo, 2015. "Estimation of ergodic agent-based models by simulated minimum distance," Journal of Economic Dynamics and Control, Elsevier, vol. 51(C), pages 148-165.
    6. Jakob Grazzini & Matteo G. Richiardi, 2013. "Consistent Estimation of Agent-Based Models by Simulated Minimum Distance," LABORatorio R. Revelli Working Papers Series 130, LABORatorio R. Revelli, Centre for Employment Studies.

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