Real-Time, Adaptive Learning via Parameterized Expectations
We explore real time, adaptive nonlinear learning dynamics in stochastic macroeconomic systems. Rather than linearizing nonlinear Euler equations where expectations play a role around a steady state, we instead approximate the nonlinear expected values using the method of parameterized expectations. Further we suppose that these approximated expectations are updated in real time as new data become available. We explore whether this method of real-time parameterized expectations learning provides a plausible alternative to real-time adaptive learning dynamics under linearized versions of the same nonlinear system.
|Date of creation:||Jul 2010|
|Date of revision:||Aug 2010|
|Contact details of provider:|| Postal: |
Web page: http://www.econ.pitt.edu/
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:pit:wpaper:400. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Alistair Wilson)
If references are entirely missing, you can add them using this form.