Structural Time Series Analyser, Modeller and Predictor
STAMP is a statistical / econometric software system for time series models with unobserved 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 8.30 is an integrated part of the OxMetrics modular software system for data analysis with excellent data manipulation 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.
Time Series Lab
Find the Signal in Your Time Series
Time Series Lab
Time Series Lab represents a family of software programs designed to model and forecast time series. Time Series Lab programs are standalone installers and do not require additional software to be installed. Time Series Lab can be downloaded from https://www.timeserieslab.com. Currently, there are three Time Series Lab programs that can be downloaded for free:
- State Space Edition
- Dynamic score Edition
- Sport statistics Edition
The Time Series Lab - State Space Edition uses the same computing engine that STAMP does, namely SsfPack. SsfPack is a set of C routines for carrying out computations involving the statistical analysis of time series models in state space form. The extremely efficient State Space algorithms result in fast optimisation times even for large systems. When working with large datasets the optimised algorithms are an important asset in keeping estimation times as low as possible.
STAMP and Time Series Lab features
|Topic||STAMP||Time Series Lab|
|Sequentially univariate analysis||Coming soon!||Development in progress|
|Multivariate analysis||✔||Development in progress|
|Fast algorithms in C||✔||✔|
|Number of seasonal components||1||3|
|Number of cycle components||3||3|
|Model forecast comparison||✖||✔|
Please contact us at email@example.com if you have additional questions about STAMP or Time Series Lab features.
Professor S.J. Koopman
Siem Jan Koopman is Professor of Econometrics at the Department of Econometrics and Data Science, Vrije Universiteit Amsterdam. He is also a research fellow at Tinbergen Institute and a long-term Visiting Professor at CREATES, University of Aarhus.
He held positions at London School of Economics and CentER (Tilburg University), and had long-term visits at US Bureau of the Census, European University Institute, and European Central Bank, Financial Research.Academic website
Professor A.C. Harvey
Andrew Harvey is Emeritus Professor of Econometrics in the Faculty of Economics and Politics, University of Cambridge. He was Professor of Econometrics at the London School of Economics before coming to Cambridge in 1996. His most recent book is a monograph entitled Dynamic Models for Volatility and Heavy Tails. Andrew Harvey is also a Fellow of the Econometric Society and a Fellow of the British Academy.Academic website
R. Lit, PhD
Rutger Lit is a research fellow at the Vrije Universiteit Amsterdam and has a PhD in econometrics, specialising in time series analysis. In 2017, he founded Nlitn, a company offering consultancy services. It also offers full data solution packages to meet the data analysis needs of clients. For example, the Time Series Lab software series.Short Bio
Jurgen A. Doornik, PhD
is a Research Fellow of Nuffield College, Oxford, UK. He is the main developer of OxMetrics, the originator of Ox, an object-oriented matrix programming language, and author (with David F. Hendry) of PcGive and PcFiml.Website