Structural time series models and the Kalman filter: a concise review

Joao Jalles

Nova SBE Working Paper Series from Universidade Nova de Lisboa, Nova School of Business and Economics

Abstract: The continued increase in availability of economic data in recent years and, more importantly, the possibility to construct larger frequency time series, have fostered the use (and development) of statistical and econometric techniques to treat them more accurately. This paper presents an exposition of structural time series models by which a time series can be decomposed as the sum of a trend, seasonal and irregular components. In addition to a detailled analysis of univariate speci?cations we also address the SUTSE multivariate case and the issue of cointegration. Finally, the recursive estimation and smoothing by means of the Kalman ?lter algorithm is described taking into account its di erent stages, from initialisation to parameters estimation.

Keywords: SUTSE; cointegration; ARIMA; smoothing; likelihood (search for similar items in EconPapers)
JEL-codes: C10 C22 C32 (search for similar items in EconPapers)
Pages: 30 pages
Date: 2009
New Economics Papers: this item is included in nep-ecm and nep-ets
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:unl:unlfep:wp541

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