Turning data into actionable insights
At Nlitn we are passionate about forecasting and econometric analysis of time series. The company was founded by successful academics who developed some of the cutting edge data analysis methods. Our dynamic team still has outstanding contacts in the academic world and we are best positioned to create value for our clients based on the latest developments at the frontiers of knowledge. The two branches of the company are consultancy and software development.
Through deep knowledge of econometric time series methodology, Nlitn consultancy works with clients to extract as much information as possible from their time series data sets. We refer to this as signal extraction and we use it to make time series evaluation and forecasts as accurate as possible. Nlitn has expertise in many areas. Among them are finance, sports, and climatology.
With the publication in top statistical journals comes a wealth of coding experience. We know how to efficiently implement financial and econometric algorithms in languages like Python, Matlab, R, OxMetrics and C. Especially regarding OxMetrics we can call ourselves experts and we work closely with its developers to extend and improve this family of software packages further.
Nlitn your data
We live in an era of big data. Many companies have a wealth of data at their disposal but do not utilize their data to the full extent. Does your company have a wealth of data? We know how to extract information from your data that is valuable for your company!
Nlitn your products
We offer full data solution packages to suit your data analyzing needs. An example is the Structural Time series Analyser, Modeller and Predictor program or STAMP in short. STAMP is a statistical / econometric software system for time series models with unobserved components such as trend, seasonal, cycle and irregular.
Rutger Lit is a time series expert who has a PhD in econometrics from the Vrije Universiteit Amsterdam. He has publications in top statistical journals and he co-authored several papers with professor S.J. Koopman, a leading scientist in the field of time series econometrics. Rutger is currently developing version 9 of the Structural Time series Analyser, Modeller and Predictor program (STAMP) which will offer some very useful new data analysing techniques. In his free time, Rutger likes to play poker and is trying to become better in climbing and bouldering.
Nlitn is proud to mention a partnership with Timberlake Consulting and Timberlake Analytics.
Timberlake has over thirty years of experience providing total and impartial solutions in statistics, econometrics and forecasting. They distribute leading software; deliver quality training courses and provide consultancy services, globally.
Together with Timberlake, Nlitn builds project specific software for the analysis of large time series databases. The software is typically used to discover complex patterns in time series which allows the customer to, for example, improve target advertising or predict future sales more accurately.
Nlitn is part of the main developing team of STAMP (Structural Time Series Analyser, Modeller and Predictor) software.
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 is an integrated part of the OxMetrics modular software system for data analysis with excellent data manipulation, graphical and batch facilities.
Version 9 of STAMP is currently under construction.
Nlitn provides Time Series Econometrics courses to a governing central bank located within Europe.
The provided courses are typically three day courses and range from basic to expert level. Dynamic modelling of time series are at the core of the courses and both theory as well as applied work is discussed. The use of time series software like STAMP and SsfPack are used to assist the customer in understanding the theory behind time series models. After the course the customer is able to apply time series theory to basic data sets.
Intraday Stochastic Volatility in Discrete Price Changes: the Dynamic Skellam Model
Koopman, S.J., Lit, R. and Lucas, A.
Published in Journal of the American Statistical Association (2017), 112, 1490-1503.
Volatility in stock prices is a statistical measure of the degree of variation in the stock price process over time. We show that intraday stochastic volatility can be extracted from discrete price changes instead of, commonly used, continuously compounded returns. We model the high-frequency tick-by-tick price changes with the discrete Skellam distribution with time-varying variance parameter. The intraday dynamics of volatility and the high number of trades without price impact require non-trivial adjustments to the basic dynamic Skellam model.
Model-Based Business Cycle and Financial Cycle Decomposition for Europe and the United States
Koopman, S.J., Lit, R. and Lucas, A.
Published in Systemic Risk Tomography: Signals, Measurements and Transmission Channels, ISTE-Elsevier, 2016.
We develop a multivariate statistical model to extract business cycle and financial cycle indicators from a panel of economic and financial time series of four large developed economies. Our model is flexible and allows for the inclusion of cycle components in different selections of economic variables with different scales and with possible phase shifts. We find clear evidence of the presence of a financial cycle with a length that is approximately twice the length of a regular business cycle.
A Dynamic Bivariate Poisson Model for Analysing and Forecasting Match Results in the English Premier League
Koopman, S.J. and Lit, R.
Published in Journal of the Royal Statistical Society, Series A (2015), 178(1), 167-186.
We develop a statistical model for the analysis and forecasting of football match results which assumes a bivariate Poisson distribution with intensity coefficients that change stochastically over time. The dynamic model is a novelty in the statistical time series analysis of match results in team sports. The out-of-sample performance of our methodology is verified in a betting strategy that is applied to the match outcomes from the 2010--2011 and 2011--2012 seasons of the English football Premier League.
Dynamic Discrete Copula Models for High Frequency Stock Price Changes
Koopman, S.J., Lit, R., Lucas, A. and Opschoor, A.
Accepted to Journal of Applied Econometrics.
We develop a dynamic model for the intraday dependence between stock price series. The model combines Skellam marginal distributions for one-second positive and negative integer price changes together with discrete copula functions to capture dependence. We consider a range of different copula functions and find strong evidence that dependence between stock price changes varies within a trading day.
Long Term Forecasting of El Niño Events via Dynamic Factor Simulations
Mengheng, Li., Koopman, S.J., Lit, R. and Desislava, P.
Working paper, 2017.
We propose a new forecasting procedure for the El Niño 3.4 time series that is linked with the well-known El Niño phenomenon. This important climate time series is subject to an intricate serial correlation structure and is related to many other relevant and related variables. Although the forecasting procedure is valid for all lead times, it is particularly developed for medium to long term forecasting of El Niño.