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 42 articles , 1 standard article )

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  1. Hindrayanto, Irma; Samarina, Anna; Stanga, Irina M.: Is the Phillips curve still alive? Evidence from the euro area (2019)
  2. Pedregal, Diego J.; Villegas, Marco A.; Villegas, Diego A.; Trapero, Juan R.: Time series modeling with Matlab: the SSpace toolbox (2019)
  3. Raphael Saavedra, Guilherme Bodin, Mario Souto: StateSpaceModels.jl: a Julia Package for Time-Series Analysis in a State-Space Framework (2019) arXiv
  4. Marco Villegas; Diego Pedregal: SSpace: A Toolbox for State Space Modeling (2018) not zbMATH
  5. Rosales Marticorena, Francisco: Empirical Bayesian smoothing splines for signals with correlated errors: methods and applications (2016)
  6. Tagore, Vickneswary; Zheng, Nan; Sutradhar, Brajendra C.: Inferences in stochastic volatility models: a new simpler way (2016)
  7. Victor Gómez: SSMMATLAB: A Set of MATLAB Programs for the Statistical Analysis of State Space Models (2015) not zbMATH
  8. Christopher Strickland; Robert Burdett; Kerrie Mengersen; Robert Denham: PySSM: A Python Module for Bayesian Inference of Linear Gaussian State Space Models (2014) not zbMATH
  9. Guillén, Osmani Teixeira de Carvalho; Issler, João Victor; Franco-Neto, Afonso Arinos de Mello: On the welfare costs of business-cycle fluctuations and economic-growth variation in the 20th century and beyond (2014)
  10. Harvey, Andrew; Luati, Alessandra: Filtering with heavy tails (2014)
  11. McElroy, Tucker; Monsell, Brian: The multiple testing problem for Box-Pierce statistics (2014)
  12. Thornton, Michael A.: Removing seasonality under a changing regime: filtering new car sales (2013)
  13. 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)
  14. Kutlu, Levent; Sickles, Robin C.: Estimation of market power in the presence of firm level inefficiencies (2012)
  15. Jacques Commandeur; Siem Koopman; Marius Ooms: Statistical Software for State Space Methods (2011) not zbMATH
  16. Jyh-Ying Peng; John Aston: The State Space Models Toolbox for MATLAB (2011) not zbMATH
  17. Roy Mendelssohn: The STAMP Software for State Space Models (2011) not zbMATH
  18. Thomas Doan: State Space Methods in RATS (2011) not zbMATH
  19. Busetti, Fabio; Harvey, Andrew: Tests of strict stationarity based on quantile indicators (2010)
  20. Lenten, Liam J. A.: Bananas and petrol: further evidence on the forecasting accuracy of the ABS ’headline’ and ’underlying’ rates of inflation (2010)

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