Features
Academic
foundation
The time series methodology of the Time Series Lab - Score Edition package is well-founded in the academic world and has appeared in highly respected academic journals. For an overview of score-driven publications we refer to the www.gasmodel.com website. The score-driven methodology was developed independently at VU University Amsterdam and Cambridge University. Currently, the knowledge and experience of both universities have been combined and Professor S.J. Koopman (VU Amsterdam) and Professor A.C. Harvey (Cambridge) are part of the Time Series Lab team.
More InformationCustomizable
software
The Time Series Lab - Score Edition software is proprietary software that can be downloaded using this link and is licensed without cost, even for commercial purposes. Make sure to read the license agreement before installing the program. Some users / companies require more specialized features of the software. Therefore, Time Series Lab - Score Edition can be made available in a commercial package, fully customized to meet the clients' needs. Please Contact us for more information about customized versions and pricing of the software.
More InformationScore-driven
methodology
Score-driven models were proposed in their full generality in Creal, Koopman, and Lucas (2013) as developed at the time at VU University Amsterdam. Simultaneously, Andrew Harvey authored the book Dynamic Models for Volatility and Heavy Tails (2013) in which the mechanism to update the parameters over time is the scaled score of the likelihood function. The score-driven model encompasses other well-known models such as the generalized autoregressive conditional heteroskedasticity (GARCH) model, autoregressive conditional duration (ACD) model, and Poisson count models with time-varying mean.
More InformationSoftware
A five step approach to extract signal from time series
Time Series Lab
This is the start-up screen of the software. Time Series Lab - Score Edition lets the user analyze and forecast a wide range of linear and non-linear time series models. Score-driven models are so versatile that well-known models like ARMA and GARCH models, are subclasses of score-driven models.
Throughout the program, info buttons 
are placed to provide the user with additional information. The text behind the info buttons is displayed when the user hoovers over the button with the mouse.
Output window
This is the core of the program. It holds buttons to the five main steps (Load data, Model setup, Estimation, Graphical Output, and Forecasting). All textual output of the program is displayed in the text window on this screen. The text output can be stored for further processing.
During estimation of the model, the program returns to this output window to monitor the progress of the estimation. Once the estimation has completed the program automatically shows the graphical output window (discussed on a later slide).
Step 1: Load data
After loading the data ('Load data' button on the left), the database headers are displayed in the 'Database' window. By clicking on the database entries, the data is displayed in the graph window. Before going to the next step (green arrow), the user can apply data transformations to the data, e.g. taking first differences. The dependent variable needs to be selected (top left) before going to the next step.
Summary characteristics are displayed in the top right corner of the graph. Graphical output can be stored via the buttons at the bottom left of the graphical window.
Step 2: Set model details
Here we construct the model by selecting the probability distribution and model components. The default model setup is a model with observations coming from the Normal distribution with time-varying level and static variance but many other compents and probability distributions can be selected. This is why our implemented score-driven methodology is so powerfull, it is extremely flexible. The specified model details are summarized in the lower part of the screen.
Step 3: Estimating the model
The model parameters are estimated by maximizing the likelihood, see also the background section. An overview of the parameters is given on this screen. The user can determine starting values for the optimizer and fix parameters to a user determined value. After estimating the model, a parameter report with standard errors and t-stats can be generated by clicking the 'Parameter report' button. The user can always abort the estimation by clicking the 'Abort' button which becomes visible during estimation. During estimation, the textual output window is visible.
Step 4: Graphical output
If the estimation of the model finished successfully, the components of the model can be inspected in the graphical output window. Components can be switched on and off with the checkboxes in the top left. Model components can be saved by clicking the 'Save all components' button. If both location and scale are time-varying, radio buttons become visible to toggle between Location and Scale components. Note that for some probability distributions only one time-varying parameter (location, scale, intensity, probability) is available.
Step 5: Forecasts
The last big step in the analysis of time series is forecasting. Time Series Lab provides a convenient way of plotting and analysing forecasts by allowing the user to dynamically change the forecast window. The loss functions used to asses the quality of the forecasts are updated with each subtraction or expansion of the forecast window.
The forecasts of each component of the model can be analyzed individually or jointly by ticking the check boxes on the top of the page.
Research
Connecting Academia and the Industry
Time Series Lab - Article Series
The Time Series Lab - Article Series are dedicated to research performed with Time Series Lab software. The scope of the series includes the analysis and forecasting of a wide range of time series in fields like economics, finance, sports, climatology, biology, and health science.
The Time Series Lab team is constantly looking for ways to improve the software. We do this by continuously working on time series theory, methodology, and applications by means of conducting our own research and studying the work of others. The Time Series Lab team would also like to hear suggestions from you on how to advance the software further. This was one of the reasons to start the Time Series Lab - Article Series
Time Series Lab - Article Series
- Harvey, A.C. and Lit, R. (2020): Coronavirus and the Score-driven Negative Binomial Distribution, Time Series Lab - Article Series, 2020(3).
- Lit, R. and Koopman S.J. (2020): Forecasting the 2020 edition of the Boat Race, Time Series Lab - Article Series, 2020(2).
- Lit, R. (2020): Forecasting the VIX in the midst of COVID-19, Time Series Lab - Article Series, 2020(1).
Other score-driven research
- Harvey, A.C. and Ito, R. (2020): Modeling time series when some observations are zero, Journal of Econometrics, 214(1), 33-45.
- Gorgi, P., Koopman, S.J. and Lit, R. (2019): The analysis and forecasting of tennis matches by using a high dimensional dynamic model, Journal of the Royal Statistical Society, Series A, 182(4), 1393-1409.
Team
Close connection with academia
R. Lit, PhD
Rutger Lit is a research fellow of Vrije Universiteit Amsterdam and has a PhD in econometrics specializing 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 package.
Personal websiteProfessor S.J. Koopman
Siem Jan Koopman is Professor of Econometrics at the Department of Econometrics, 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.
Personal websiteProfessor 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 2013 monograph entitled Dynamic Models for Volatility and Heavy Tails.
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