The ACR Model: A Multivariate Dynamic Mixture Autoregression.
This paper proposes and analyses the autoregressive conditional root (ACR) time-series model. This multivariate dynamic mixture autoregression allows for non-stationary epochs. It proves to be an appealing alternative to existing nonlinear models, e.g. the threshold autoregressive or Markov switchin...
| Main Authors: | , , |
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| Format: | Journal article |
| Language: | English |
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Blackwell Publishing
2008
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| _version_ | 1797076231882014720 |
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| author | Bec, F Rahbek, A Shephard, N |
| author_facet | Bec, F Rahbek, A Shephard, N |
| author_sort | Bec, F |
| collection | OXFORD |
| description | This paper proposes and analyses the autoregressive conditional root (ACR) time-series model. This multivariate dynamic mixture autoregression allows for non-stationary epochs. It proves to be an appealing alternative to existing nonlinear models, e.g. the threshold autoregressive or Markov switching class of models, which are commonly used to describe nonlinear dynamics as implied by arbitrage in presence of transaction costs. Simple conditions on the parameters of the ACR process and its innovations are shown to imply geometric ergodicity, stationarity and existence of moments. Furthermore, consistency and asymptotic normality of the maximum likelihood estimators are established. An application to real exchange rate data illustrates the analysis. |
| first_indexed | 2024-03-07T00:01:10Z |
| format | Journal article |
| id | oxford-uuid:760126c9-2576-4610-83fe-a3fe543a66da |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T00:01:10Z |
| publishDate | 2008 |
| publisher | Blackwell Publishing |
| record_format | dspace |
| spelling | oxford-uuid:760126c9-2576-4610-83fe-a3fe543a66da2022-03-26T20:12:54ZThe ACR Model: A Multivariate Dynamic Mixture Autoregression.Journal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:760126c9-2576-4610-83fe-a3fe543a66daEnglishDepartment of Economics - ePrintsBlackwell Publishing2008Bec, FRahbek, AShephard, NThis paper proposes and analyses the autoregressive conditional root (ACR) time-series model. This multivariate dynamic mixture autoregression allows for non-stationary epochs. It proves to be an appealing alternative to existing nonlinear models, e.g. the threshold autoregressive or Markov switching class of models, which are commonly used to describe nonlinear dynamics as implied by arbitrage in presence of transaction costs. Simple conditions on the parameters of the ACR process and its innovations are shown to imply geometric ergodicity, stationarity and existence of moments. Furthermore, consistency and asymptotic normality of the maximum likelihood estimators are established. An application to real exchange rate data illustrates the analysis. |
| spellingShingle | Bec, F Rahbek, A Shephard, N The ACR Model: A Multivariate Dynamic Mixture Autoregression. |
| title | The ACR Model: A Multivariate Dynamic Mixture Autoregression. |
| title_full | The ACR Model: A Multivariate Dynamic Mixture Autoregression. |
| title_fullStr | The ACR Model: A Multivariate Dynamic Mixture Autoregression. |
| title_full_unstemmed | The ACR Model: A Multivariate Dynamic Mixture Autoregression. |
| title_short | The ACR Model: A Multivariate Dynamic Mixture Autoregression. |
| title_sort | acr model a multivariate dynamic mixture autoregression |
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