Signal Extraction

Signal extraction is about the definition and the estimation of interesting signals in time series. In practice, attention is often focussed on the current boundary of time series: does the seasonally adjusted unemployment rate increase or decrease, do we observe a turning-point in the business-cycle, should we keep, buy or sell an asset based on the current momentum? We here analyse the structure of prospective real-time signal extraction problems by emphasizing trend estimation and derive specific (efficient) solutions for level-approximations as well as or for the early detection of turning-points.

Our contribution and therefore the novelty of our approach, lies in the scrupulous matching of optimization criteria and tests to the underlying problem structures. The latest iteration of the book on real-time signal extraction conveys a comprehensive treatment of the subject. Additionally, selected articles, projects, presentations and pieces of software code give access to more detailed aspects of our work on the topic. These techniques are currently in use in leading economic indicators, in governmental analysis tools as well as in trading algorithms. Current developments include very promising non-linear and multivariate extensions of our original approach.

• M. Wildi (Version as of 30 Jun 2008) Real-Time Signal Extraction - Beyond Maximum Likelihood Principles
• M. Wildi (Version as of 26 Nov 2007) Real-Time Signal Extraction - Beyond Maximum Likelihood Principles
• M. Wildi (Version as of 20 Oct 2007) Real-Time Signal Extraction - Beyond Maximum Likelihood Principles
• M. Wildi (2006) Signalextraction - Efficient Estimation, 'Unit-Root'-Tests and Early Detection of Turning-Points, Springer.

• Wildi, M. (2008) Customized Forecasting Criteria or 'How to Win Forecasting Competitions?', IDP Working Paper, IDP-WP-08Sep-05
• Wildi, M. and Elmer, S. (2008) Real-Time Filtering and Turning-Point Detection: Application of Customized Criteria (DFA) to the US Business Cycle, IDP Working Paper, IDP-WP-08Sep-04
• Wildi, M. and Hoenecke, O. (2008) Real-Time Filtering: Non-Linear DFA and Asymmetry of the US Business Cycle, IDP-Working paper, IDP-WP-08Sep-03
• Wildi, M. and Sturm, J.-E. (2008) Real-Time Filtering: Using the Multivariate DFA to Monitor the US Business Cycle, IDP-Working paper, IDP-WP-08Sep-02
• Wildi, M. (2008) Efficient Multivariate Real-Time Filtering and Cointegration, IDP-Working paper, IDP-WP-08Sep-01
• Perrin, D. and Wildi, M. (2008) Statistical Modeling of Writing Processes, IDP Working Paper, IDP-WP-07Aug-01
• A collection of other papers on signal extraction by Marc Wildi

• Case study on signal extraction with R-package source code
• Link to CRAN (select Packages => signalextraction)

• KOF new economic barometer

• IASCA 2008 (5-8 December 2008)
• CIRET 2008 (8-11 October 2008)
• 5th EUROSTAT Colloquium on modern tools in business cycle analysis (28 Sept - 1 Oct 2008)
• IDP Colloquium (27 November 2007)
• Beijing Academy of Sciences (12-19 November 2007)
• CIRANO Workshop Montréal (5-6 October 2007)
• KOF Conference on real-time series, Zurich (9 July 2007)
• Rmetrics Seminar Meielisalp, Switzerland (July 2007)
• International Symposium on Forecasting, New York, USA (24-27 June 2007)
• ERCIM Workshop Geneva 2007 (20-22 April 2007)
• German Ministry of Technology and Economy, Berlin (2007)
• Conference EUROSTAT on leading indicators (2007)
• Conference EUROSTAT on seasonal adjustment, Luxembourg (10-12 May 2006)
• International Symposium on Forecasting, Santander, Spain (2006)
• IFO Conference on Survey Data, Munich (14-15 October 2005)
• International Symposium on Forecasting, San Antonio, Texas, USA (2005)