STAMP is a statistical / econometric software system for time series models with unobserbed components such as trend, seasonal, cycle and irregular. It provides a user-friendly environment for the analysis, modelling and forecasting of time series. Estimation and signal extraction is carried out using state space methods and Kalman filtering. However, STAMP is set up in an easy-to-use form which enables the user to concentrate on model selection and interpretation. STAMP 8 is an integrated part of the OxMetrics modular software system for data analysis with excellent data manipulation, graphical and batch facilities. The full name of STAMP is Structural Time Series Analyser, Modeller and Predictor. Structural time series models are formulated directly in terms of components of interest and also therefore often referred to as unobserved component time series models. Such models find application in many subjects, including economics, finance, sociology, management science, biology, geography, meteorology and engineering. STAMP bridges the gap between the theory and its application; providing the necessary tool to make interactive structural time series modelling available for empirical work. Another such tool is SsfPack, which provides more general procedures for the programming interface Ox.

References in zbMATH (referenced in 17 articles )

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  1. Rosales Marticorena, Francisco: Empirical Bayesian smoothing splines for signals with correlated errors: methods and applications (2016)
  2. McElroy, Tucker; Monsell, Brian: The multiple testing problem for Box-Pierce statistics (2014)
  3. Krieg, Sabine; den Brakel, Jan A.Van: Estimation of the monthly unemployment rate for six domains through structural time series modelling with cointegrated trends (2012)
  4. Harvey, Andrew C.; Delle Monache, Davide: Computing the mean square error of unobserved components extracted by misspecified time series models (2009)
  5. Jungbacker, Borus; Koopman, Siem Jan: Parameter estimation and practical aspects of modeling stochastic volatility (2009)
  6. Bujosa, Marcos; GarcĂ­a-Ferrer, Antonio; Young, Peter C.: Linear dynamic harmonic regression (2007)
  7. Commandeur, Jacques J. F.; Koopman, Siem Jan: An introduction to state space time series analysis. (2007)
  8. Ooms, Marius; Doomik, Jurgen A.: Econometric software development: past, present and future (2006)
  9. Penzer, Jeremy: Diagnosing seasonal shifts in time series using state space models (2006)
  10. Pollock, D.S.G.: Econometric methods of signal extraction (2006)
  11. Pollock, D.S.G. (ed.): Introduction to the special issue on statistical signal extraction and filtering (2006)
  12. Trimbur, Thomas M.: Properties of higher order stochastic cycles (2006)
  13. Koopman, Siem Jan; Ooms, Marius: Time series modelling of daily tax revenues. (2003)
  14. Ghysels, Eric; Osborn, Denise R.: The econometric analysis of seasonal time series. With a foreword by Thomas J. Sargent (2001)
  15. Pollock, D.S.G.: Trend estimation and de-trending via rational square-wave filters (2000)
  16. Moosa, Imad A.: Testing the currency-substitution model under the German hyperinflation (1999)
  17. Harvey, Andrew; Streibel, Mariane: Testing for a slowly changing level with special reference to stochastic volatility (1998)