Transformations of the State Variable and Learning Dynamics

Shurojit Chatterji and Ignacio Lobato

No 708, Working Papers from Centro de Investigacion Economica, 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.

Keywords: Temporary equilibrium; Ordinary least squares learning; Globally stable formulations (search for similar items in EconPapers)
Pages: 35 pages
Date: 2007-12
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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http://ftp.itam.mx/pub/academico/inves/lobato/07-08.pdf First version, 2007 (application/pdf)

Related works:
Journal Article: Transformations of the state variable and learning dynamics (2010)
Working Paper: Transformations of the State Variable and Learning Dynamics (2009)
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Persistent link: https://EconPapers.repec.org/RePEc:cie:wpaper:0708

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