On Divergent Dynamics with Ordinary Least Squares Learning
This article addresses the stability properties of a simple economy (characterized by a one-dimensional state variable) when the representative agent, confronted by trajectories that are divergent from the steady state, performs transformations in that variable in order to improve forecasts. We find...
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sg-smu-ink.soe_research-26392016-03-13T12:47:50Z On Divergent Dynamics with Ordinary Least Squares Learning CHATTERJI, Shurojit LOBATO, Ignacio This article addresses the stability properties of a simple economy (characterized by a one-dimensional state variable) when the representative agent, confronted by trajectories that are divergent from the steady state, performs transformations in that variable in order to improve forecasts. We find that instability continues to be a robust outcome for transformations such as differencing and detrending the data, the two most typical approaches in econometrics to handle nonstationary time series data. We also find that inverting the data, a transformation that can be motivated by the agent reversing the time direction in an attempt to improve her forecasts, may lead the dynamics to a perfect-foresight path. 2015-01-01T08:00:00Z text https://ink.library.smu.edu.sg/soe_research/1640 info:doi/10.1016/j.jebo.2014.10.003 https://doi.org/10.1016/j.jebo.2014.10.003 Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Temporary equilibrium Ordinary least squares learning Stability Economics Economic Theory |
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Temporary equilibrium Ordinary least squares learning Stability Economics Economic Theory CHATTERJI, Shurojit LOBATO, Ignacio On Divergent Dynamics with Ordinary Least Squares Learning |
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This article addresses the stability properties of a simple economy (characterized by a one-dimensional state variable) when the representative agent, confronted by trajectories that are divergent from the steady state, performs transformations in that variable in order to improve forecasts. We find that instability continues to be a robust outcome for transformations such as differencing and detrending the data, the two most typical approaches in econometrics to handle nonstationary time series data. We also find that inverting the data, a transformation that can be motivated by the agent reversing the time direction in an attempt to improve her forecasts, may lead the dynamics to a perfect-foresight path. |
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text |
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CHATTERJI, Shurojit LOBATO, Ignacio |
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CHATTERJI, Shurojit LOBATO, Ignacio |
author_sort |
CHATTERJI, Shurojit |
title |
On Divergent Dynamics with Ordinary Least Squares Learning |
title_short |
On Divergent Dynamics with Ordinary Least Squares Learning |
title_full |
On Divergent Dynamics with Ordinary Least Squares Learning |
title_fullStr |
On Divergent Dynamics with Ordinary Least Squares Learning |
title_full_unstemmed |
On Divergent Dynamics with Ordinary Least Squares Learning |
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on divergent dynamics with ordinary least squares learning |
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Institutional Knowledge at Singapore Management University |
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2015 |
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https://ink.library.smu.edu.sg/soe_research/1640 https://doi.org/10.1016/j.jebo.2014.10.003 |
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