Learning,parameter variability,and swings in US macroeconomic dynamics |
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| Institution: | 1. Faculty of Economic Sciences, University of Warsaw, Poland;2. Group for Research in Applied Economics (GRAPE), Faculty of Management, University of Warsaw IZA, Poland;3. National Bank of Poland, Poland;1. School of Economics, Shandong University, No. 27 ShandaNanlu, Jinan City, 250100 Shandong Province, P.R. China;2. Department of Economics, University of California, Riverside, CA, 92521, USA;3. Department of Economics and Related Studies, University of York, Heslington, York, YO10 5DD, UK;2. Humboldt-Universität zu Berlin, CEPR and IZA;3. International Monetary Fund |
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| Abstract: | Recent studies show that the estimated parameters of rational expectations dynamic stochastic general equilibrium models of the business cycle are largely time-varying. This paper shows that assuming adaptive learning (rather than rational expectations) strongly reduces the estimated parameter variability of standard models (by around 75%). Moreover, the reduction in parameter variability induced by adaptive learning is much stronger for the subsets of parameters that control nominal price and wage rigidity and the subset of policy rule parameters (at 98% and 83%, respectively). Furthermore, our estimation results suggest that adaptive learning helps to explain the recent swings in the comovements between real and nominal US macroeconomic variables, but the swing in the relative weight of supply and demand shocks seems to be the most important driving force. |
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