Bayesian Model Averaging, Learning and Model Selection
Agents have two forecasting models, one consistent with the unique rational expectations equilibrium, another that assumes a time-varying parameter structure. When agents use Bayesian updating to choose between models in a self-referential system, we find that learning dynamics lead to selection of one of the two models. However, there are parameter regions for which the non-rational forecasting model is selected in the long-run. A key structural parameter governing outcomes measures the degree of expectations feedback in Muth's model of price determination.
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- Thomas J. Sargent & Noah William, 2005.
"Impacts of Priors on Convergence and Escapes from Nash Inflation,"
Review of Economic Dynamics,
Elsevier for the Society for Economic Dynamics, vol. 8(2), pages 360-391, April.
- Thomas J. Sargent & Noah Williams, 2003. "Impacts of priors on convergence and escapes from Nash inflation," Working Paper 2003-14, Federal Reserve Bank of Atlanta.
- Timothy Cogley & Thomas J. Sargent, 2005.
"The conquest of US inflation: Learning and robustness to model uncertainty,"
Review of Economic Dynamics,
Elsevier for the Society for Economic Dynamics, vol. 8(2), pages 528-563, April.
- Cogley, Timothy & Sargent, Thomas J., 2005. "The conquest of U.S. inflation: learning and robustness to model uncertainty," Working Paper Series 0478, European Central Bank.
- McGough, Bruce, 2003. "Statistical Learning With Time-Varying Parameters," Macroeconomic Dynamics, Cambridge University Press, vol. 7(01), pages 119-139, February.
- Young, H. Peyton, 2004. "Strategic Learning and its Limits," OUP Catalogue, Oxford University Press, number 9780199269181, March.
- Bullard, James, 1992. "Time-varying parameters and nonconvergence to rational expectations under least squares learning," Economics Letters, Elsevier, vol. 40(2), pages 159-166, October.
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