Publicação
Uncertainty quantification with a Gaussian Process Prior : an example from macroeconomics
| dc.contributor.advisor | Paulo, Rui | |
| dc.contributor.author | Tavares, Ivo Alberto Valente | |
| dc.date.accessioned | 2021-06-09T13:00:03Z | |
| dc.date.available | 2021-06-09T13:00:03Z | |
| dc.date.issued | 2021 | |
| dc.description | Doutoramento em Matemática Aplicada à Economia e Gestão | pt_PT |
| dc.description.abstract | This thesis may be broadly divided into 4 parts. In the first part, we do a literature review of the state of the art in misspecification in Macroeconomics, and what so far has been the contribution of a relatively new area of research called Uncertainty Quantification to the Macroeconomics subject. These reviews are essential to contextualize the contribution of this thesis in the furthering of research dedicated to correcting non-linear misspecifications, and to account for several other sources of uncertainty, when modelling from an economic perspective. In the next three parts, we give an example, using the same simple DSGE model from macroeconomic theory, of how researchers may quantify uncertainty in a State-Space Model using a discrepancy term with a Gaussian Process prior. The second part of the thesis, we used a full Gaussian Process (GP) prior on the discrepancy term. Our experiments showed that despite the heavy computational constraints of our full GP method, we still managed to obtain a very interesting forecasting performance with such a restricted sample size, when compared with similar uncorrected DSGE models, or corrected DSGE models using state of the art methods for time series, such as imposing a VAR on the observation error of the state-space model. In the third part of our work, we improved on the computational performance of our previous method, using what has been referred in the literature as Hilbert Reduced Rank GP. This method has close links to Functional Analysis, and the Spectral Theorem for Normal Operators, and Partial Differential Equations. It indeed improved the computational processing time, albeit just slightly, and was accompanied with a similarly slight decrease in the forecasting performance. The fourth part of our work delved into how our method would account for model uncertainty just prior, and during, the great financial crisis of 2007-2009. Our technique allowed us to capture the crisis, albeit at a reduced applicability possibly due to computational constraints. This latter part also was used to deepen the understanding of our model uncertainty quantification technique with a GP. Identifiability issues were also studied. One of our overall conclusions was that more research is needed until this uncertainty quantification technique may be used in as part of the toolbox of central bankers and researchers for forecasting economic fluctuations, specially regarding the computational performance of either method. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.citation | Tavares, Ivo Alberto Valente (2021). "Uncertainty quantification with a Gaussian Process Prior : an example from macroeconomics". Tese de Doutoramento, Universidade de Lisboa. Instituto Superior de Economia e Gestão. | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10400.5/21444 | |
| dc.language.iso | eng | pt_PT |
| dc.publisher | Instituto Superior de Economia e Gestão | pt_PT |
| dc.subject | Uncertainty Quantification | pt_PT |
| dc.subject | Machine-learning | pt_PT |
| dc.subject | Misspecification | pt_PT |
| dc.subject | Non-Linearities | pt_PT |
| dc.subject | Structural Uncertainty | pt_PT |
| dc.subject | Model Discrepancy | pt_PT |
| dc.subject | Gaussian Processes | pt_PT |
| dc.subject | Hilbert Methods | pt_PT |
| dc.subject | Financial Crisis | pt_PT |
| dc.subject | State-Space Models | pt_PT |
| dc.title | Uncertainty quantification with a Gaussian Process Prior : an example from macroeconomics | pt_PT |
| dc.type | doctoral thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | doctoralThesis | pt_PT |