State Space Models in R
We give an overview of some of the software tools available in R, either as built- in functions or contributed packages, for the analysis of state space models. Several illustrative examples are included, covering constant and time-varying models for both univariate and multivariate time series. Max...
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Foundation for Open Access Statistics
2011-05-01
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| Series: | Journal of Statistical Software |
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| Online Access: | http://www.jstatsoft.org/v41/i04/paper |
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doaj-3b020d121d31421087d8065bb2c30f4f2020-11-24T22:03:57ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602011-05-014104State Space Models in RGiovanni PetrisSonia PetroneWe give an overview of some of the software tools available in R, either as built- in functions or contributed packages, for the analysis of state space models. Several illustrative examples are included, covering constant and time-varying models for both univariate and multivariate time series. Maximum likelihood and Bayesian methods to obtain parameter estimates are considered.http://www.jstatsoft.org/v41/i04/paperKalman filterstate space modelsunobserved componentssoftware toolsR |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Giovanni Petris Sonia Petrone |
| spellingShingle |
Giovanni Petris Sonia Petrone State Space Models in R Journal of Statistical Software Kalman filter state space models unobserved components software tools R |
| author_facet |
Giovanni Petris Sonia Petrone |
| author_sort |
Giovanni Petris |
| title |
State Space Models in R |
| title_short |
State Space Models in R |
| title_full |
State Space Models in R |
| title_fullStr |
State Space Models in R |
| title_full_unstemmed |
State Space Models in R |
| title_sort |
state space models in r |
| publisher |
Foundation for Open Access Statistics |
| series |
Journal of Statistical Software |
| issn |
1548-7660 |
| publishDate |
2011-05-01 |
| description |
We give an overview of some of the software tools available in R, either as built- in functions or contributed packages, for the analysis of state space models. Several illustrative examples are included, covering constant and time-varying models for both univariate and multivariate time series. Maximum likelihood and Bayesian methods to obtain parameter estimates are considered. |
| topic |
Kalman filter state space models unobserved components software tools R |
| url |
http://www.jstatsoft.org/v41/i04/paper |
| work_keys_str_mv |
AT giovannipetris statespacemodelsinr AT soniapetrone statespacemodelsinr |
| _version_ |
1725831309520011264 |