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Economic Hysteresis with Multiple Inputs– a Simplified Treatment

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  • Matthias Göcke

    (Justus Liebig University of Giessen)

Abstract

Hysteresis in economics is up to now usually based on a representation of a system with only a single input variable, which has a persistent effect on an economic outcome (i.e. the output variable). However, in general there is more than one factor influencing economic decision problems, why the description of the path-dependency in relation to only one input variable may (possibly) be insufficient. The multidimensional path-dependence phenomenon is addressed (in mathematics and physics) by a vector-hysteresis system, with an input vector of two ore more variables. Unfortunately, these models are quite complicated for practical purposes in economics. However, since standard economic decisions are based on comparing economic values of alternatives (e.g. present values of investments), this can be used to reduce the dimensions of the hysteresis system. This paper outlines how the influence of several original input variables (e.g. price level and interest rate) is captured by the resulting variations of the present value of an investment. This economic value then can be used as a single signal/input variable of a modified hysteresis system. Since this system is dimensionally reduced to the standard hysteresis case with only a single input variable, the standard aggregation procedure for a situation with heterogeneous agents can be applied again.

Suggested Citation

  • Matthias Göcke, 2018. "Economic Hysteresis with Multiple Inputs– a Simplified Treatment," MAGKS Papers on Economics 201801, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    • Handle: RePEc:mar:magkse:201801
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    More about this item

    Keywords

    vector-hysteresis; sunk-cost hysteresis; path-dependence; non-ideal relay; Mayergoyz/Preisach-model.;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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