Multilevel time series modelling of mobility trends in the Netherlands for small domains
| Autor: | Sumonkanti Das, J. A. van den Brakel, Harm Jan Boonstra |
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| Přispěvatelé: | QE Econometrics, RS: GSBE Theme Data-Driven Decision-Making, RS: GSBE other - not theme-related research |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Statistics and Probability
Economics and Econometrics hierarchical Bayesian models small area estimation INFORMATION LEVEL structural time series models Classification of discontinuities Set (abstract data type) symbols.namesake Small area estimation Econometrics SMALL-AREA ESTIMATION Mathematics INCOME Series (mathematics) global local shrinkage Markov chain Monte Carlo MCMC simulation official statistics HIERARCHICAL BAYES ESTIMATION Standard error Travel survey Outlier symbols Statistics Probability and Uncertainty PRIOR DISTRIBUTIONS Social Sciences (miscellaneous) |
| Zdroj: | Journal of the Royal Statistical Society Series A-Statistics in Society, 184(3), 985-1007. Wiley-Blackwell |
| ISSN: | 0964-1998 |
| Popis: | The purpose of the Dutch Travel Survey is to produce reliable estimates on mobility of the Dutch population. In this paper mobility trends are estimated at several aggregation levels, using multilevel time series models. The models account for discontinuities induced by two survey redesigns and outliers due to less reliable outcomes in one particular year. The input for the model is direct annual estimates with their standard errors for the period 1999–2017 for a detailed cross-classification in 504 domains. Appropriate transformations for the direct estimates and generalized variance functions to smooth the standard errors of the direct estimates are proposed. The models are fitted in an hierarchical Bayesian framework using MCMC simulations. From the model outputs smooth trend estimates are computed at the most detailed domain level. Predictions at higher aggregation levels obtained by aggregation of the most detailed domain predictions result in a numerically consistent set of trend estimates for all target variables. |
| Databáze: | OpenAIRE |
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