The convergence of least squares learning in stochastic temporary equilibrium models
This paper provides conditions for the almost sure convergence of the least squares learning rule in a stochastic temporary equilibrium model, where regressions are performed on the past values of the endogenous state variable. In contrast to earlier studies, (Evans and Honkapohja, 1998; Marcent and...
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2002
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| Online Access: | https://ink.library.smu.edu.sg/soe_research/1882 |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | This paper provides conditions for the almost sure convergence of the least squares learning rule in a stochastic temporary equilibrium model, where regressions are performed on the past values of the endogenous state variable. In contrast to earlier studies, (Evans and Honkapohja, 1998; Marcent and Sargent, 1989), which were local analyses, the dynamics are studied from a global viewpoint, which allows one to obtain an almost sure convergence result without employing projection facilities. |
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