Time Series Analysis for the State-Space Model with R/Stan / / by Junichiro Hagiwara

• Record Nr.: UNINA9910495161903321
• Autore: Hagiwara Junichiro
• Pubbl/distr/stampa: Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2021
• ISBN: 9789811607110; 9811607117
• Edizione: [1st ed. 2021.]
• Descrizione fisica: 1 online resource (350 pages)
• Disciplina: 519.55
• Soggetti: Statistics; Mathematical statistics - Data processing; Econometrics; Macroeconomics; Applied Statistics; Statistics and Computing; Bayesian Inference; Statistical Theory and Methods; Quantitative Economics; Macroeconomics and Monetary Economics
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Introduction -- Fundamental of probability and statistics -- Fundamentals of handling time series data with R -- Quick tour of time series analysis -- State-space model -- State estimation in the state-space model -- Batch solution for linear Gaussian state-space model -- Sequential solution for linear Gaussian state-space model -- Introduction and analysis examples of a well-known component model -- Batch solution for general state-space model -- Sequential solution for general state-space model -- Example of applied analysis in general state-space model.
• Sommario/riassunto: This book provides a comprehensive and concrete illustration of time series analysis focusing on the state-space model, which has recently attracted increasing attention in a broad range of fields. The major ; feature of the book lies in its consistent Bayesian treatment regarding whole combinations of batch and sequential solutions for linear Gaussian and general state-space models: MCMC and Kalman/particle filter. The reader is given insight on flexible modeling in modern time series analysis. The main topics of the book deal with the state-space model, covering extensively, from introductory and exploratory methods to the latest advanced topics such as real-time structural change detection. Additionally, a practical exercise using R/Stan based on real data promotes understanding and enhances the reader’s analytical capability. .