Essays in Regime Switching Policy and Adaptive Learning in Dynamic Stochastic General Equilibrium
| dc.contributor.advisor | Evans, George | |
| dc.contributor.author | McClung, Nigel | |
| dc.date.accessioned | 2018-09-06T21:53:31Z | |
| dc.date.available | 2018-09-06T21:53:31Z | |
| dc.date.issued | 2018-09-06 | |
| dc.description.abstract | This dissertation studies monetary-fiscal policy interactions and adaptive learning applications in regime-switching DSGE models. A common thread through my research is understanding how policymakers may be affected by the interaction of policy regime change and agents' beliefs about past, current or future policy in general equilibrium. The work I present in this dissertation shows that conventional and unconventional policy outcomes, as well as the existence, uniqueness and expectational stability of rational expectations solutions, depend heavily on the expectational effects of time-varying policy. These findings suggest that uncertainty over future fiscal policy may curb the effectiveness of monetary policy, or otherwise constrain the actions of central bankers. In carrying out this research agenda, my work also examines the relationship between determinacy and expectational stability in a general class of Markov-switching DSGE models. | en_US |
| dc.identifier.uri | https://hdl.handle.net/1794/23707 | |
| dc.language.iso | en_US | |
| dc.publisher | University of Oregon | |
| dc.rights | All Rights Reserved. | |
| dc.subject | Adaptive learning | en_US |
| dc.subject | Determinacy | en_US |
| dc.subject | E-stability | en_US |
| dc.subject | Fiscal policy | en_US |
| dc.subject | Markov-switching | en_US |
| dc.subject | Monetary policy | en_US |
| dc.title | Essays in Regime Switching Policy and Adaptive Learning in Dynamic Stochastic General Equilibrium | |
| dc.type | Electronic Thesis or Dissertation | |
| thesis.degree.discipline | Department of Economics | |
| thesis.degree.grantor | University of Oregon | |
| thesis.degree.level | doctoral | |
| thesis.degree.name | Ph.D. |
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