2023
DOI: 10.3390/forecast6010003
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Bootstrapping State-Space Models: Distribution-Free Estimation in View of Prediction and Forecasting

José Francisco Lima,
Fernanda Catarina Pereira,
Arminda Manuela Gonçalves
et al.

Abstract: 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 b… Show more

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