Messy Time Series: A Unified Approach - (Now published in 'Advances in Econometrics', 13 (1998)pp.103-143.)

Andrew Harvey, Siem Jan Koopman and J Penzer

STICERD - Econometrics Paper Series from Suntory and Toyota International Centres for Economics and Related Disciplines, LSE

Abstract: Many series are subject to data irregularities such as missing values, outliers, structural breaks and irregular spacing. Data can also be messy, and hence difficult to handle by standard procedures, when they are intrinsically non-Gaussian or contain complicated periodic patterns because they are observed on an hourly or weekly basis. This paper presents a unified approach to the analysis of messy data. The technical treatment is based on state space methods. These methods can be applied to any linear model including those from the autoregressive integrated moving average class. However, the ease of interpretation of structural time series models, together with the associated information produced by the Kalman filter and smoother, makes them a more natural vehicle for dealing with messy data. Structural time series models can also be formulated in continuous time thereby allowing for a general treatment of irregularly spaced observations. The periodic patterns associated with hourly or weekly data can be dealt with effectively using time-varying splines while the statistical analysis of non-Gaussian models is now feasible because of recent developments in simulation techniques.

Keywords: ARIMA models; DATA aggregation; importance sampling; irregularly spaced data; Kalman filter; missing observations; outliers; smoother; splines; state space form; structural breaks; structural time series models; weekly observations. (search for similar items in EconPapers)
Date: 1997-03
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cep:stiecm:327

Access Statistics for this paper

More papers in STICERD - Econometrics Paper Series from Suntory and Toyota International Centres for Economics and Related Disciplines, LSE
Bibliographic data for series maintained by (sticerd@lse.ac.uk).