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Workshop on Recent Advances in Nonlinear Time Series Analysis
(7 - 18 February 2011)

Organizing Committee · Visitors and Participants · Overview · Activities · Venue

 

 Organizing Committee

 

Chair

 

Co-Chairs

 

 Visitors and Participants

 

 

 Overview

 

In Statistics, Nonlinear Time Series Analysis started around 1980. After a slow start, its development has been rapid recently. The following events have provided timely and important momentum to the development of the subject.

 

  1. Research Workshop on Nonlinear Time Series Analysis and Applications Held in Edinburgh on 12-25 July 1989 (with 10 papers published in a special issue on Nonlinear Time Series Analysis, Statistica Sinica, volume 1, 1991);
  2. The Royal Statistical Society Chaos Day in London in 1991 (with 7 papers and discussions published in Series B of the Journal of the RSS in 1992);
  3. The Royal Society (London) Discussion Meeting on Chaos and Forecasting in 1994 (with 15 papers and discussions published in the Transactions of the Royal Society, 1994);
  4. The International Workshop on Financial Statistics in Hong Kong in 1999 (with proceedings published by Imperial College Press/World Scientific in 2000);
  5. The International conference on Threshold Models and New Developments in Time Series, celebrating Howell Tong’s 60th birthday, in Hong Kong in 2004 (with 13 papers published in Statistica Sinica, 2007).

 

Important specialist monographs have also appeared steadily over the past twenty years or so, notably Priestley (1989), Tong (1983, 1990), Chan and Tong (2001), Fan and Yao (2003) and others.

 

The need to break away from linear models was recognized almost as early as 1927 when Udny Yule introduced the first linear time series model, namely the autoregressive model. The fact that it took statisticians more than half a century to accomplish the first breakthrough might seem strange at first sight. However, if we bear in mind the following historical perspectives, then we might begin to appreciate the enormity of the task. The inevitable complexity of a nonlinear time series model necessitates computing power not easily available before the 1970s. The relevant theory in probability theory to handle the ergodicity/stationarity issues of a Markov chain over Rp was not available till around the mid-1970s. Some relevant statistical methodologies, such as model selection, conditional least squares and others, were only fully developed in the 1970s. In the deterministic world, even the notion of chaos had to wait till 1976 before it took off.

 

After the initial breakthroughs in the early 1980s, progress was still slow over the decade, due to the enormity of the theoretical and methodological challenges. Fortunately, with successful applications in diverse fields, e.g. ecology, economics, epidemiology, finance, hydrology and many others, and interaction with dynamicists, an increasing number of bright and young statisticians started to be attracted to the exciting area in the late 1980s and early 1990s. As a result, publications have grown exponentially. Just as an illustration, according to the ISI Web of Knowledge, the citations of a threshold time series model have reached about 1,000 in 2009 since its birth in 1980. The corresponding figures in 1990 and 2000 were approximately 10 and 250 respectively!

 

Many exciting developments are indeed taking place. The relatively recent entry of non-parametric and semi-parametric techniques into the time series analysis has given modern time series analysts powerful tools to explore the time series data from many different perspectives, before deciding on an appropriate parametric model for more in-depth exploration. Nonlinear dimension reduction methodology is being developed and exploited to handle high-dimensional time series data. Various factor models are being developed to cope with high frequency data, so often found in financial applications. The tantalizing connection between nonlinearity and long-memory is yet to be fully investigated. The connection between non-linear stochastic differential equations and discrete-time nonlinear time series models awaits careful study. The almost unique signature of nonlinear time series analysis is its challenge to our ability to bring the most relevant probability theory, statistical methodology, dynamical system knowledge and computing ingenuity to bear so as to get a clearer glimpse at the dynamical world around us.

 

In Singapore and especially at the NUS, there are clusters of expertise in the methodology of nonlinear time series analysis with particular reference to applications in finance, infectious diseases and environmental impacts on public health. Some of the local expertise has reached the international level and others approaching it.

 

The main mission of the workshop is to bring together international researchers with expertise in the different areas listed earlier, namely non-parametrics, semi-parametrics, dimension reduction, high-dimensional time series, and many others, so as to gain a deeper and wider understanding of the dynamical world. By participating in this exciting venture, local nonlinear enthusiasts will have ample opportunities of interacting with international experts at the highest level. There is no doubt that the workshop will be an important milestone in the development of nonlinear time series analysis on an international scale. It will also help bring about a quantum leap in research in non-linear time series analysis in Singapore.

 

 Activities

 


Tuesday, 8 Feb 2011

09:15am - 09:30am

Registration

09:30am - 09:35am

Opening Remarks

 

Session I

09:35am - 12:30pm

Feature matching in time series modelling

Howell Tong, London School of Economics, UK
 

Estimating extremal dependence in time series via the extremogram

Richard Davis, Columbia University, USA

12:30pm - 02:00pm

--- Lunch ---

 

Session II

02:00pm ~

Modeling and estimation for nonstationary financial time series

Ying Chen,National University of Singapore

Wednesday, 9 Feb 2011

09:50am - 10:00am

Registration

 

Session I

10:00am - 12:30pm

 

 

 

 

Particle filter based on-line estimation of spot and cross volatility with non-linear market micro-structure noise models (PDF)

Rainer Dahlhaus, University of Heidelberg, Germany

 

Stochastic covariance models (PDF)

Mike, Ka Pui So, Hong Kong University of Science and Technology, Hong Kong

12:30pm - 02:00pm

--- Lunch ---

 

Session II

02:00pm ~

Inference for some continuous-time models in finance

Peter Brockwell, Colorado State University, USA and University of Melbourne, Australia

Thursday, 10 Feb 2011

09:50am - 10:00am

Registration

 

Session I

10:00am - 12:30pm

A new fractional IGARCH model

Guodong Li, University of Hong Kong, Hong Kong
 

Quantile estimation of threshold autoregressive models with exogenous variables and heteroskedasticity

Cathy W. S. Chen, Feng Chia University, Taiwan

12:30pm - 02:00pm

--- Lunch ---

 

Session II

02:00pm ~

Plague

Noelle Samia, Northwestern University, USA

Friday, 11 Feb 2011

09:50am - 10:00am

Registration

 

Session I

10:00am - 12:30pm

Statistical modelling of nonlinear long-term cumulative effects

Yingcun Xia, National University of Singapore

 

Factor modelling for high dimensional time series

Qiwei Yao, London School of Economics, UK

12:30pm - 02:00pm

--- Lunch ---

 

Session II

02:00pm ~

A general framework for identification of time-varying dynamic systems using multiple models

Cheng Xiang, National University of Singapore

Monday, 14 Feb 2011

09:50am - 10:00am

Registration

 

Session I

10:00am - 11:30am

Modelling nonlinear time series with spatial neighbouring effects: a personal review

Zudi Lu, University of Adelaide, Australia

11:30am - 02:00pm

--- Welcome lunch reception at IMS ---

 

Session II

02:00pm - 03:30pm

Goodness of fit tests for time series based on scores (PDF)

Shiqing Ling, Hong Kong University of Science and Technology, Hong Kong

Tuesday, 15 Feb 2011

09:50am - 10:00am

Registration

 

Session I

10:00am - 11:30am

On least squares estimation of multiple-regime TAR models (PDF)

Dong Li, Hong Kong University of Science and Technology, Hong Kong

11:30am - 02:00pm

--- Lunch ---

 

Session II

02:00pm - 03:30pm

A backward stochastic differential equation approach to convex risk measures for derivative securities

Ken Siu, Macquarie University, Australia

Wednesday, 16 Feb 2011

09:50am - 10:00am

Registration

 

Session I

10:00am - 11:30am

Estimation of a threshold autoregressive model under misspecification

Myung Hwan Seo, London School of Economics, UK 

11:30am - 02:00pm

--- Lunch ---

 

Session II

02:00pm - 03:30pm

Quasi-likelihood estimation of threshold diffusion processes

Kung-Sik Chan, University of Iowa, USA

Thursday, 17 Feb 2011

09:50am - 10:00am

Registration

 

Session I

10:00am - 11:30am

Quantiles, spectral analysis and time series

Marc Hallin, Université Libre de Bruxelles, Belgium 

11:30am - 02:00pm

--- Lunch ---

 

Session II

02:00pm - 03:30pm

Mixture Kalman filter and plug-and-play navigation systems

Rong Chen, Rutgers University, USA

Friday, 18 Feb 2011

09:50am - 10:00am

Registration

 

Session I

10:00am - 11:00am

Test for nonlinearity based on entropy measure

Simone Giannerini, University of Bologna, Italy

11:00pm - 11:30am

--- Coffee Break ---

11:30am - 12:30pm

Trending time seies and non- and semi-parametric cointegration

Jiti Gao, The University of Adelaide, Australia

12:30pm - 02:00pm

--- Lunch ---

 

Open Forum

02:00pm - 03:30pm

Open Forum I: Future directions of nonlinear time series analysis

 

Open Forum II: More future directions of nonlinear time series analysis


Students and researchers who are interested in attending these activities and who do not require financial aid are requested to complete the online registration form.

The following do not need to register:

  • Those invited to participate.
  • Those applying for financial support.


 Venue

 

 

Organizing Committee · Visitors and Participants · Overview · Activities · Venue

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