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 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.
least squares learning, almost sure convergence
Springer Verlag (Germany)
The convergence of least squares learning in stochastic temporary equilibrium models. (2002). Economic Theory. 20, (4), 837-847. Research Collection School Of Economics.
Available at: http://ink.library.smu.edu.sg/soe_research/1882
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