Bootstrapping State-Space Models: Distribution-Free Estimation in View of Prediction and Forecasting
Linear models, seasonal autoregressive integrated moving average (SARIMA) models, and state-space models have been widely adopted to model and forecast economic data. While modeling using linear models and SARIMA models is well established in the literature, modeling using state-space models has bee...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Forecasting |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2571-9394/6/1/3 |
| _version_ | 1827306164341178368 |
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| author | José Francisco Lima Fernanda Catarina Pereira Arminda Manuela Gonçalves Marco Costa |
| author_facet | José Francisco Lima Fernanda Catarina Pereira Arminda Manuela Gonçalves Marco Costa |
| author_sort | José Francisco Lima |
| collection | DOAJ |
| description | Linear models, seasonal autoregressive integrated moving average (SARIMA) models, and state-space models have been widely adopted to model and forecast economic data. While modeling using linear models and SARIMA models is well established in the literature, modeling using state-space models has been extended with the proposal of alternative estimation methods to the maximum likelihood. However, maximum likelihood estimation assumes, as a rule, that the errors are normal. This paper suggests implementing the bootstrap methodology, utilizing the model’s innovation representation, to derive distribution-free estimates—both point and interval—of the parameters in the time-varying state-space model. Additionally, it aims to estimate the standard errors of these parameters through the bootstrap methodology. The simulation study demonstrated that the distribution-free estimation, coupled with the bootstrap methodology, yields point forecasts with a lower mean-squared error, particularly for small time series or when dealing with smaller values of the autoregressive parameter in the state equation of state-space models. In this context, distribution-free estimation with the bootstrap methodology serves as an alternative to maximum likelihood estimation, eliminating the need for distributional assumptions. The application of this methodology to real data showed that it performed well when compared to the usual maximum likelihood estimation and even produced prediction intervals with a similar amplitude for the same level of confidence without any distributional assumptions about the errors. |
| first_indexed | 2024-04-24T18:16:51Z |
| format | Article |
| id | doaj.art-5d7a83c455144e6f913f5d3f8eea8ba8 |
| institution | Directory Open Access Journal |
| issn | 2571-9394 |
| language | English |
| last_indexed | 2024-04-24T18:16:51Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forecasting |
| spelling | doaj.art-5d7a83c455144e6f913f5d3f8eea8ba82024-03-27T13:41:22ZengMDPI AGForecasting2571-93942023-12-0161365410.3390/forecast6010003Bootstrapping State-Space Models: Distribution-Free Estimation in View of Prediction and ForecastingJosé Francisco Lima0Fernanda Catarina Pereira1Arminda Manuela Gonçalves2Marco Costa3Department of Mathematics, University of Minho, 4710-057 Braga, PortugalCentre of Mathematics, University of Minho, 4710-057 Braga, PortugalDepartment of Mathematics, University of Minho, 4710-057 Braga, PortugalCentre for Research and Development in Mathematics and Applications, Águeda School of Technology and Management, University of Aveiro, 3810-193 Aveiro, PortugalLinear models, seasonal autoregressive integrated moving average (SARIMA) models, and state-space models have been widely adopted to model and forecast economic data. While modeling using linear models and SARIMA models is well established in the literature, modeling using state-space models has been extended with the proposal of alternative estimation methods to the maximum likelihood. However, maximum likelihood estimation assumes, as a rule, that the errors are normal. This paper suggests implementing the bootstrap methodology, utilizing the model’s innovation representation, to derive distribution-free estimates—both point and interval—of the parameters in the time-varying state-space model. Additionally, it aims to estimate the standard errors of these parameters through the bootstrap methodology. The simulation study demonstrated that the distribution-free estimation, coupled with the bootstrap methodology, yields point forecasts with a lower mean-squared error, particularly for small time series or when dealing with smaller values of the autoregressive parameter in the state equation of state-space models. In this context, distribution-free estimation with the bootstrap methodology serves as an alternative to maximum likelihood estimation, eliminating the need for distributional assumptions. The application of this methodology to real data showed that it performed well when compared to the usual maximum likelihood estimation and even produced prediction intervals with a similar amplitude for the same level of confidence without any distributional assumptions about the errors.https://www.mdpi.com/2571-9394/6/1/3bootstrapdistribution-free estimationeconomic dataforecastingstate-space modelingtime series analysis |
| spellingShingle | José Francisco Lima Fernanda Catarina Pereira Arminda Manuela Gonçalves Marco Costa Bootstrapping State-Space Models: Distribution-Free Estimation in View of Prediction and Forecasting Forecasting bootstrap distribution-free estimation economic data forecasting state-space modeling time series analysis |
| title | Bootstrapping State-Space Models: Distribution-Free Estimation in View of Prediction and Forecasting |
| title_full | Bootstrapping State-Space Models: Distribution-Free Estimation in View of Prediction and Forecasting |
| title_fullStr | Bootstrapping State-Space Models: Distribution-Free Estimation in View of Prediction and Forecasting |
| title_full_unstemmed | Bootstrapping State-Space Models: Distribution-Free Estimation in View of Prediction and Forecasting |
| title_short | Bootstrapping State-Space Models: Distribution-Free Estimation in View of Prediction and Forecasting |
| title_sort | bootstrapping state space models distribution free estimation in view of prediction and forecasting |
| topic | bootstrap distribution-free estimation economic data forecasting state-space modeling time series analysis |
| url | https://www.mdpi.com/2571-9394/6/1/3 |
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