Essays in Behavioral Macroeconomics
| dc.contributor.advisor | McGough, Bruce | |
| dc.contributor.author | Thompson, Jacob | |
| dc.date.accessioned | 2024-08-07T22:12:59Z | |
| dc.date.available | 2024-08-07T22:12:59Z | |
| dc.date.issued | 2024-08-07 | |
| dc.description.abstract | This dissertation investigates a class of DSGE models with bounded rationality where agents use recursively updated forecasts to form expectations of future vari- ables The two chapters explore the implications of the model builder’s choice of initial forecasting model with which to endow agents. Each chapter estimates a different New Keynesian DSGE model, varying this initial model and finds that this has sub- stantial impacts on parameter estimates as well as the ability of the model to fit macroeconomic data series. Chapter 1 estimates a small scale, purely forward-looking DSGE model but relaxes the assumption of rational expectations. In so doing, it outlines the computational challenges of estimating such a model and the solutions thereto. It also introduces the reader to a new class of Bayesian posterior sampler called Sequential Monte Carlo which has key advantages over Markov Chain Monte Carlo samplers for the estimation of models with Adaptive Learning. I find two notable results: first, I find that one can greatly improve the ability of the model to explain the data by training agents’ initial forecasting model on pre-sample data. Second, I find that, for this particular DSGE model, the estimated slope of the Phillips Curve is significantly greater than under Rational Expectations. Chapter 2 estimates a small-scale DSGE model with habit persistence in household consumption and inflation indexation by price-setting firm, thereby inducing mechan- ical persistence in both the output and inflation processes. This chapter shows that the improved data-fit from training sample based initial beliefs is robust to the in- clusion of mechanical lags. It also shows how initial forecasting models trained on pre-sample data cause the DSGE model to exhibit impulse response functions that show the “price puzzle” despite the additional restrictions of the DSGE model, and what restrictions to impose to avoid this outcome. | en_US |
| dc.identifier.uri | https://hdl.handle.net/1794/29803 | |
| dc.language.iso | en_US | |
| dc.publisher | University of Oregon | |
| dc.rights | All Rights Reserved. | |
| dc.subject | Adaptive Learning | en_US |
| dc.subject | Bayesian Econometrics | en_US |
| dc.subject | Behavioral Economics | en_US |
| dc.subject | Bounded Rationality | en_US |
| dc.subject | DSGE | en_US |
| dc.subject | Macroeconomics | en_US |
| dc.title | Essays in Behavioral Macroeconomics | |
| 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. |
Files
Original bundle
1 - 1 of 1
Loading...
- Name:
- Thompson_oregon_0171A_13928.pdf
- Size:
- 2.41 MB
- Format:
- Adobe Portable Document Format