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Dr M.B. Eichler

Research projects
  • Causal inference for multivariate time series with latent variables
    In applications involving multiple time series, the concept of Granger causality is frequently used to describe the causal relationships among the variables. Although the interest in Granger causality has been increased, for instance, in neuroscience, its suitability as a measure for causality is much disputed. The objective of my research is to clarify the meaning and limitations of Granger causality and to develop algorithms for causal inference from time series data that take these limitations into account.
  • Dependence analysis in multiple heterogeneous time series
    The objective of this project, carried out in cooperation with Ratheon BBN Technologies (USA), is to predict/detect rare events of significance on a population level from multiple heterogeneous openly available data sources (including e.g. social network data). Within this task, our project focuses on the development and investigation of methods for aggregating and extracting relevant information from multiple heterogeneous time series where particular attention will be given to approaches based on Granger causality, dynamic factor modelling, and model averaging. This project is part of the open source indicator (OSI) program financed by the US government agency IARPA.
    PhD student: Carlos A. Moreno
  • Electricity spot price modelling
    Electricity spot prices feature a number of stylized facts such as mean reversion, strong seasonality, and frequent occurrence of short periods of extreme prices. These price spikes occur more often in markets with compulsary market participations such as the Australian NEM market. The objective of this project is to develop new approaches for modelling and forecasting electricity spot prices and spikes.
    PhD student: Dennis Tuerk
  • Semi-parametric dynamics factor models for non-stationary processes
    Current approaches for fitting dynamic factor models to nonstationary time series are based on dynamic principal components analysis in the frequency domain. These approaches are fully nonparametric and depend strongly on the chosen bandwidths for smoothing over frequency and time. In this project, a semi-parametric approach in which only parts of the model are allowed to be time-varying is investigated.
    PhD student: Anne van Delft
Recent publications
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Other publications
  • Empirical spectral processes and their applications to stationary point processes. Annals of Applied Probability 5 (1995), 1161-1176.
  • Identification of synaptic connections in neural ensembles by graphical models (with R. Dahlhaus and J. Sandkühler). Journal of Neuroscience Methods 77 (1997), 93-107.
  • Cross-spectral analysis of tremor time series (with J. Timmer, M. Lauk, B. Köster, B. Hellwig, S. Häußler, B. Guschlbauer, V. Radt, M. Eichler, G. Deuschl, C.H. Lücking). International Journal of Bifurcation and Chaos 10 (2000), 2595-2610.
  • Partial correlation analysis for the identification of synaptic connections (with R. Dahlhaus, J. Sandkühler). Biological Cybernetics 89 (2003), 289-302.
  • Causality and graphical models for time series (with R. Dahlhaus). In: P. Green, N. Hjort, and S. Richardson (eds), Highly structured stochastic systems (2003), University Press, Oxford, pp. 115-137.
  • A graphical approach for evaluating effective connectivity in neural systems. Philosophical Transactions of The Royal Society B 360 (2005), 953-967. (previously: Graphical time series modelling in brain imaging)
  • Maximum likelihood estimation in Gaussian chain graph models under the alternative Markov property (with M. Drton). Scandinavian Journal of Statistics 33 (2006), 247-257.
  • On the evaluation of information flow in multivariate systems based on the directed transfer function. Biological Cybernetics 94 (2006), 469-482.
  • Testing for directed influences in neuroscience using partial directed coherence (with B. Schelter, M. Winterhalder, M. Peifer, B. Hellwig, B. Guschlbauer, C.H. Lücking, R. Dahlhaus, J. Timmer). Journal of Neuroscience Methods 152 (2006), 210-219.
  • Graphical modelling of dynamic relationships in multivariate time series. In: M. Winterhalder, B. Schelter, J. Timmer (eds), Handbook of Time Series Analysis (2006), Wiley-VCH, Berlin, pp. 335-372.
  • Fitting graphical interaction models to multivariate time series. Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence (2006), AUAI Press.
  • A frequency-domain based test for independence between stationary time series. Metrika 65 (2007), 133-157.
  • Granger-causality and path diagrams for multivariate time series. Journal of Econometrics 137 (2007), 334-353.
  • Causal reasoning in graphical time series models (with V. Didelez). In Proceedings of the 23rd Conference on Uncertainty in Artificial Intelligence (2007).
  • Dynamic analysis of electronic diary data of obese patients with and without binge eating disorder (with B. Wild, S. Feiler, H.-J. Friederich, M. Hartmann, W. Herzog, S. Zipfel). Psychotherapy and Psychosomatics 76 (2007), 250-252.
  • Testing nonparametric and semiparametric hypotheses in vector stationary processes. Journal of Multivariate Analysis 99 (2008), 968-1009. [DOI:10.1016/j.jmva.2007.06.003]
  • Computing maximum likelihood estimates in recursive linear models (with M. Drton and T.S. Richardson). Journal of Machine Learning Research 10 (2009), 2329-2348. [arXiv:math.ST/0601631]
  • Assessing the strength of directed influences among neural signals using renormalized partial directed coherence (with B. Schelter and J. Timmer) Journal of Neuroscience Methods 179 (2009), 121-130. [DOI:10.1016/j.jneumeth.2009.01.006]
  • Estimating causal dependencies in networks of nonlinear stochastic dynamical systems (with L. Sommerlade, M. Jachan, K. Henschel, J. Timmer, B. Schelter) Physical Review E 80 (2009), 051128. [DOI:10.1103/PhysRevE.80.051128]
  • Causal inference from multivariate time series: What can be learned from Granger causality. In: C. Glymour, W. Wang, D. Westerstahl (eds), Logic, Methodology and Philosophy of Science. Proceedings of the 13th International Congress (2009), College Publications, London.
  • On Granger-causality and the effect of interventions in time series (with V. Didelez). Life time data analysis 16 (2010), 3-32. [DOI:10.1007/s10985-009-9143-3]
  • A graphical vector autoregressive modelling approach to the analysis of electronic diary data (with B. Wild, H.-C. Friederich, M. Hartmann, S. Zipfel, W. Herzog). BMC Medical Research Methodology 10:28 (2010). [DOI:10.1186/1471-2288-10-28]
  • Graphical Gaussian modelling of multivariate time series with latent variables. Journal of Machine Learning Research W&CP 9 (2010), 193-200.
  • Fitting dynamic factor models to non-stationary time series (with G. Motta and R. von Sachs). Journal of Econometrics 163 (2011), 51-70. [DOI:10.1016/j.jeconom.2010.11.007]
  • Graphical modelling of multivariate time series. Probability Theory and Related Fields 153 (2012), 233-268. [DOI:10.1007/s00440-011-0345-8]
  • Causal inference in time series analysis. In: C. Berzuini, A.P. Dawid, L. Bernardinelli (eds), Causality: Statistical Perspectives and Applications (2012), Wiley, Chichester.
  • Identifiability of regular and singular multivariate autoregressive models from mixed frequency data (with B.D.O. Anderson, M. Deistler, E. Felsenstein, B. Funovits, P. Zadrony, W. Chen, M. Zamani). In: Proceedings of the 51st IEEE Conference on Decision and Control (2012).
  • Fitting semiparametric Markov regime-switching models to electricity spot prices (with D. Türk). Energy Economics 36 (2013), 614-624. [DOI:10.1016/j.eneco.2012.11.013]
  • Causal inference with multiple time series: principles and problems. Philosophical Transaction of The Royal Society A 371 (2013), 20110612. [DOI:10.1098/rsta.2011.0613]
  • The impact of latent confounders in directed network analysis in neuroscience (with R. Ramb, M. Eichler, A. Ing, M. Thiel, C. Weiller, C. Grebogi, Ch. Schwarzbauer, J. Timmer, and B. Schelter). Philosophical Transaction of The Royal Society A 371 (2013), 20110613. [DOI:10.1098/rsta.2011.0612]
  • Models for short-term forecasting of spike occurrences in Australian electricity markets: a comparitive study (with O. Grothe, H. Manner, and D. Türk). To appear in: The Journal of Energy Markets.