Diagnosing shocks in time series

Piet de Jong*, Jeremy Penzer

*Corresponding author for this work

Research output: Contribution to journalArticle

62 Citations (Scopus)

Abstract

Efficient means of modeling aberrant behavior in times series are developed. Our methods are based on state-space forms and allow test statistics for various interventions to be computed from a single run of the Kalman filter smoother. The approach encompasses existing detection methodologies. Departures commonly observed in practice, such as outlying values, level shifts, and switches, are readily dealt with. New diagnostic statistics are proposed. Implications for structural models, autoregressive integrated moving average models, and models with explanatory variables are given.

Original languageEnglish
Pages (from-to)796-806
Number of pages11
JournalJournal of the American Statistical Association
Volume93
Issue number442
DOIs
Publication statusPublished - 1 Jun 1998
Externally publishedYes

Keywords

  • Dynamic regression models
  • Interventions
  • Kalman filter
  • Outliers
  • Smoothing
  • State-space models

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