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 30 articles )

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  1. Rosales Marticorena, Francisco: Empirical Bayesian smoothing splines for signals with correlated errors: methods and applications (2016)
  2. Harvey, Andrew; Luati, Alessandra: Filtering with heavy tails (2014)
  3. McElroy, Tucker; Monsell, Brian: The multiple testing problem for Box-Pierce statistics (2014)
  4. Thornton, Michael A.: Removing seasonality under a changing regime: filtering new car sales (2013)
  5. 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)
  6. Kutlu, Levent; Sickles, Robin C.: Estimation of market power in the presence of firm level inefficiencies (2012)
  7. Busetti, Fabio; Harvey, Andrew: Tests of strict stationarity based on quantile indicators (2010)
  8. Lenten, Liam J.A.: Bananas and petrol: further evidence on the forecasting accuracy of the ABS ’headline’ and ’underlying’ rates of inflation (2010)
  9. Luati, Alessandra; Proietti, Tommaso: Hyper-spherical and elliptical stochastic cycles (2010)
  10. Harvey, Andrew C.; Delle Monache, Davide: Computing the mean square error of unobserved components extracted by misspecified time series models (2009)
  11. Jungbacker, Borus; Koopman, Siem Jan: Parameter estimation and practical aspects of modeling stochastic volatility (2009)
  12. Franco, Glaura C.; Santos, Thiago R.; Ribeiro, Juliana A.; Cruz, F.R.B.: Confidence intervals for the hyperparameters in structural models (2008)
  13. Bujosa, Marcos; García-Ferrer, Antonio; Young, Peter C.: Linear dynamic harmonic regression (2007)
  14. Commandeur, Jacques J. F.; Koopman, Siem Jan: An introduction to state space time series analysis. (2007)
  15. Penzer, Jeremy: State space models for time series with patches of unusual observations (2007)
  16. Ooms, Marius; Doomik, Jurgen A.: Econometric software development: past, present and future (2006)
  17. Penzer, Jeremy: Diagnosing seasonal shifts in time series using state space models (2006)
  18. Pollock, D.S.G.: Econometric methods of signal extraction (2006)
  19. Pollock, D.S.G. (ed.): Introduction to the special issue on statistical signal extraction and filtering (2006)
  20. Trimbur, Thomas M.: Properties of higher order stochastic cycles (2006)

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