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Transformations of the State Variable and Learning Dynamics

Author

Listed:
  • Shurojit Chatterji

    () (Centro de Investigacion Economica (CIE), Instituto Tecnologico Autonomo de Mexico (ITAM))

  • Ignacio Lobato

    () (Centro de Investigacion Economica (CIE), Instituto Tecnologico Autonomo de Mexico (ITAM))

Abstract

This article studies dynamics in a model where agents forecast a one dimensional variable via ordinary least squares regressions on the lagged values of the state variable. We study the stability properties of alternative transformations of the state variable that the agent can endogenously set forth. We study the consequences on the economy's stability of the typical transformations that an econometrician would attemp, such as differencing, detrending, or taking instantaneous concave transformations, such as logarithms. Surprinsingly, for the considered class of economies, we found that these transformations are destabilizing, whereas alternative transformations, which an econometrician would never consider, such as convex transformations, are stabilizing. Therefore, we ironically find that in our set-up. an active agent, who is concerned about learning the economy's dynamics and transforms the state variable, in an attempt to improve forecasting, is more likely to deviate from the steady state than a passive agent.

Suggested Citation

  • Shurojit Chatterji & Ignacio Lobato, 2007. "Transformations of the State Variable and Learning Dynamics," Working Papers 0708, Centro de Investigacion Economica, ITAM.
  • Handle: RePEc:cie:wpaper:0708
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    References listed on IDEAS

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    Cited by:

    1. Chatterji, Shurojit & Lobato, Ignacio N., 2015. "On divergent dynamics with ordinary least squares learning," Journal of Economic Behavior & Organization, Elsevier, vol. 109(C), pages 1-9.

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    Keywords

    Temporary equilibrium; Ordinary least squares learning; Globally stable formulations;

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