Publication
Smooth transition regression models: theory and applications in JMulti
| dc.contributor.author | Menezes, Rui | |
| dc.contributor.author | Ferreira, Nuno | |
| dc.contributor.author | Souza, Adriano Mendonça | |
| dc.contributor.author | Souza, F. M. | |
| dc.date.accessioned | 2024-01-09T11:50:49Z | |
| dc.date.available | 2024-01-09T11:50:49Z | |
| dc.date.issued | 2020-12-29 | |
| dc.date.updated | 2024-01-08T19:40:05Z | |
| dc.description.abstract | This tutorial aims to analyze nonlinear models of Smooth Transition Regression with JMulTi and contribute to the understanding of STR specification, from the estimation until the evaluation cycle of these models. It provides pedagogical explanations, combining theoretical concepts and empirical results coherently. Especially in economic relationships, where an asymmetric behaviour with distinct effects is often found on contractions and expansions. As economic series generally present asymmetric/nonlinear behaviour, Smooth Transition Regression (STR) models provide a flexible empirical strategy that allows capturing the impacts of possible types of asymmetry in the data, Souza (2016).An overview of theory and applications in software is described. These nonlinear models describe in-sample movements of the stock returns series better than the corresponding linear model. The data used in this study consist of daily prices index from January 02, 1995 to March 29, 2013, a total of 4761 observations, from Germany (DAX30). The data was collected from the DataStream database considering 5 days a week. The data (price index) is converted to base 100 and the yields are then calculated based on the first differences in the log price series. 10-year interest rates treasury bond regarding the same markets identified has also been collected for the same period. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.citation | Menezes, R., Ferreira, N., Souza, A. M., & Souza, F. M. (2020). Smooth Transition Regression models: Theory and Applications in JMulti. Ciência e Natura, 42, 18. https://doi.org/10.5902/2179460X40466 | pt_PT |
| dc.identifier.doi | 10.5902/2179460X40466 | pt_PT |
| dc.identifier.issn | 2179-460X | |
| dc.identifier.slug | cv-prod-3033473 | |
| dc.identifier.uri | http://hdl.handle.net/10400.26/48754 | |
| dc.language.iso | eng | pt_PT |
| dc.title | Smooth transition regression models: theory and applications in JMulti | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.endPage | e18 | pt_PT |
| oaire.citation.startPage | e18 | pt_PT |
| oaire.citation.title | Ciência e Natura | pt_PT |
| oaire.citation.volume | 42 | pt_PT |
| person.familyName | Mendonça Souza | |
| person.givenName | Francisca | |
| person.identifier.ciencia-id | DD15-B860-0C26 | |
| person.identifier.orcid | 0000-0003-3011-0086 | |
| rcaap.cv.cienciaid | DD15-B860-0C26 | Francisca Mendonça Souza | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | article | pt_PT |
| relation.isAuthorOfPublication | c21c2cb7-fd5c-4e64-b44b-8be82785102e | |
| relation.isAuthorOfPublication.latestForDiscovery | c21c2cb7-fd5c-4e64-b44b-8be82785102e |
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