Predictions in Time Series Using Regression Models:
Saved in:
| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
New York, NY
Springer New York
2002
|
| Subjects: | |
| Online Access: | Volltext |
| Item Description: | Books on time series models deal mainly with models based on Box-Jenkins methodology which is generally represented by autoregressive integrated moving average models or some nonlinear extensions of these models, such as generalized autoregressive conditional heteroscedasticity models. Statistical inference for these models is well developed and commonly used in practical applications, due also to statistical packages containing time series analysis parts. The present book is based on regression models used for time series. These models are used not only for modeling mean values of observed time se ries, but also for modeling their covariance functions which are often given parametrically. Thus for a given finite length observation of a time series we can write the regression model in which the mean value vectors depend on regression parameters and the covariance matrices of the observation depend on variance-covariance parameters. Both these dependences can be linear or nonlinear. The aim of this book is to give an unified approach to the solution of statistical problems for such time series models, and mainly to problems of the estimation of unknown parameters of models and to problems of the prediction of time series modeled by regression models |
| Physical Description: | 1 Online-Ressource (IX, 233 p) |
| ISBN: | 9781475736298 9781441929655 |
| DOI: | 10.1007/978-1-4757-3629-8 |
Staff View
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| illustrated | Not Illustrated |
| indexdate | 2024-07-10T01:21:09Z |
| institution | BVB |
| isbn | 9781475736298 9781441929655 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-027856919 |
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| physical | 1 Online-Ressource (IX, 233 p) |
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| publishDate | 2002 |
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| spelling | Štulajter, František Verfasser aut Predictions in Time Series Using Regression Models by František Štulajter New York, NY Springer New York 2002 1 Online-Ressource (IX, 233 p) txt rdacontent c rdamedia cr rdacarrier Books on time series models deal mainly with models based on Box-Jenkins methodology which is generally represented by autoregressive integrated moving average models or some nonlinear extensions of these models, such as generalized autoregressive conditional heteroscedasticity models. Statistical inference for these models is well developed and commonly used in practical applications, due also to statistical packages containing time series analysis parts. The present book is based on regression models used for time series. These models are used not only for modeling mean values of observed time se ries, but also for modeling their covariance functions which are often given parametrically. Thus for a given finite length observation of a time series we can write the regression model in which the mean value vectors depend on regression parameters and the covariance matrices of the observation depend on variance-covariance parameters. Both these dependences can be linear or nonlinear. The aim of this book is to give an unified approach to the solution of statistical problems for such time series models, and mainly to problems of the estimation of unknown parameters of models and to problems of the prediction of time series modeled by regression models Statistics Finance Mathematical statistics Economics / Statistics Econometrics Statistical Theory and Methods Statistics for Business/Economics/Mathematical Finance/Insurance Quantitative Finance Statistik Wirtschaft Regressionsmodell (DE-588)4127980-3 gnd rswk-swf Zeitreihenanalyse (DE-588)4067486-1 gnd rswk-swf Zeitreihenanalyse (DE-588)4067486-1 s Regressionsmodell (DE-588)4127980-3 s 1\p DE-604 https://doi.org/10.1007/978-1-4757-3629-8 Verlag Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Štulajter, František Predictions in Time Series Using Regression Models Statistics Finance Mathematical statistics Economics / Statistics Econometrics Statistical Theory and Methods Statistics for Business/Economics/Mathematical Finance/Insurance Quantitative Finance Statistik Wirtschaft Regressionsmodell (DE-588)4127980-3 gnd Zeitreihenanalyse (DE-588)4067486-1 gnd |
| subject_GND | (DE-588)4127980-3 (DE-588)4067486-1 |
| title | Predictions in Time Series Using Regression Models |
| title_auth | Predictions in Time Series Using Regression Models |
| title_exact_search | Predictions in Time Series Using Regression Models |
| title_full | Predictions in Time Series Using Regression Models by František Štulajter |
| title_fullStr | Predictions in Time Series Using Regression Models by František Štulajter |
| title_full_unstemmed | Predictions in Time Series Using Regression Models by František Štulajter |
| title_short | Predictions in Time Series Using Regression Models |
| title_sort | predictions in time series using regression models |
| topic | Statistics Finance Mathematical statistics Economics / Statistics Econometrics Statistical Theory and Methods Statistics for Business/Economics/Mathematical Finance/Insurance Quantitative Finance Statistik Wirtschaft Regressionsmodell (DE-588)4127980-3 gnd Zeitreihenanalyse (DE-588)4067486-1 gnd |
| topic_facet | Statistics Finance Mathematical statistics Economics / Statistics Econometrics Statistical Theory and Methods Statistics for Business/Economics/Mathematical Finance/Insurance Quantitative Finance Statistik Wirtschaft Regressionsmodell Zeitreihenanalyse |
| url | https://doi.org/10.1007/978-1-4757-3629-8 |
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