CFE-ERCIM 2011
P
ROGRAMME AND
A
BSTRACTS
5th CSDA International Conference on
Computational and Financial Econometrics (CFE 2011)
http://www.cfe-csda.org/cfe11and
4th International Conference of the
ERCIM (European Research Consortium for Informatics and Mathematics) Working Group on
Computing & Statistics (ERCIM 2011)
http://www.cfe-csda.org/ercim11Senate House, University of London, UK
17-19 December 2011
http://www.qmul.ac.uk http://www.bbk.ac.uk http://www.lse.ac.uk
CFE-ERCIM 2011
CFE 2011 International Organizing Committee:
Ana-Maria Fuertes, George Kapetanios, Erricos J. Kontoghiorghes, Herman K. Van Dijk and Qiwei Yao.
CFE 2011 Co-chairs:
Hashem Pesaran, Siem Jan Koopman, Monica Billio, Christian Francq and Mike So.
CFE 2011 Programme Committee:
A. Amendola, R F. Audrino, C. Baum, L. Bauwens, M. Binder, F. Canova, A. Carriero, C.W.S. Chen, L.
Calvet, R. Gerlach, D. Guegan, L. Khalaf, C.M. Kuan, M. Lippi, M. McAleer, G.M. Martin, Y. Omori, T. Perez
-Amaral, D.S.G. Pollock, M. Reale, M. Riani, J. Rombouts, E. Ruiz, W. Semmler, M. Steel, L. Soegner,
E. Tzavalis, D. Veredas, I. Vrontos, M. Wagner, P. Zadrozny, P. Zaffaroni, J.M. Zakoian and A. Zeileis.
CSDA Annals of CFE Managing Editors:
David A. Belsley, Erricos J. Kontoghiorghes and Herman K. Van Dijk.
ERCIM 2011 International Organizing Committee:
Stan Azen, Ana Colubi, Erricos J. Kontoghiorghes, George Loizou and Irini Moustaki.
ERCIM 2011 Co-chairs:
Christophe Croux, Roland Fried, Steve Gilmour, Domingo Morales and Tommaso Proietti.
ERCIM 2011 Programme Committee:
E. Ahmed, A.M. Alonso, A.C. Atkinson, A. Christmann, T. Denoeux, M. Falk, P. Foschi, C. Gatu,
L.A. Garcia-Escudero, R. Gerlach, M.I. Gomes, G. Gonzalez-Rodriguez, A. Gordaliza, A. Grane, C.
Hen-nig, J. Hinde, M. Huber, T. Kneib, S. Kuhnt, J. Lopez-Fidalgo, A. Luati, A. Mayo, S. Meintanis, I. Molina,
M.C. Pardo, R.W. Payne, C. Perna, S. Petrone, I. Poli, I. Pruenster, S. Sanfelici, G. Storti, S. Van Aelst,
I. Van Keilegom, J. Whittaker, P. Winker, R. Zamar and A. Zeileis.
Local Organizer:
Department of Economics, Queen Mary, University of London, UK.
Department of Statistics, London School of Economics, UK.
Birkbeck, University of London, UK.
CFE-ERCIM 2011
CS53: BAYESIAN METHODS IN ECONOMETRIC AND FINANCIAL APPLICATIONS(Room: B36) . . . 55
CS61: LONG MEMORY TIME SERIES MODELS(Room: B18) . . . 56
CS64: EFFICIENTMCMCALGORITHMS FORBAYESIAN FINANCIAL ECONOMETRIC MODELS(Room: B20) . . . 57
Parallel Session I – ERCIM (Sunday 18.12.2011 at 11:05 - 12:45) 58 ESI03: ADVANCES IN MULTIVARIATE ANALYSIS(Room: Woburn) . . . 58
ES01: ROBUST ANALYSIS OF COMPLEX DATA SETS(Room: Senate) . . . 58
ES04: TIMESERIES MODELING AND COMPUTATIONI (Room: Bloomsbury) . . . 59
ES23: ADVANCES IN DISTANCE-BASED METHODS AND APPLICATIONS(Room: Gordon) . . . 59
ES24: MIXTURE MODELS:APPLICATIONS AND EXTENSIONS(Room: Torrington) . . . 60
ES28: DESIGN AND ANALYSIS OF COMPUTER EXPERIMENTSI (Room: Bedford) . . . 60
ES30: BIOSTATISTICSI (Room: S264) . . . 61
ES33: ROBUST METHODS IN SMALL AREA ESTIMATION(Room: S261) . . . 62
ES36: PARAMETRIC AND SEMIPARAMETRIC HAZARDS MODELS AND ANALYSES(Room: Jessel) . . . 62
ES59: IMPRECISE PROBABILISTIC MODELING TO SOLVE STATISTICAL PROBLEMSII (Room: Court) . . . 63
Parallel Session J – CFE (Sunday 18.12.2011 at 14:15 - 16:20) 65 CS07: ADVANCES IN COMPUTATIONAL METHODS FORDSGEMODELS(Room: B33) . . . 65
CS38: SIGNAL EXTRACTION AND FORECASTING(Room: B36) . . . 65
CS42: DYNAMIC CORRELATION MODELS(Room: G16) . . . 66
CS50: BAYESIAN MODEL AVERAGING IN ECONOMETRICS(Room: B34) . . . 67
CS63: COMPUTATIONAL AND ECONOMETRIC METHODS IN DERIVATIVES APPLICATIONS(Room: B35) . . . 67
CS69: MEASURING SYSTEMIC RISK(Room: B20) . . . 68
CS92: FINANCIAL ECONOMETRICSII (Room: B18) . . . 69
CP03: POSTERS SESSIONIII (Room: Chancellor’s) . . . 69
Parallel Session J – ERCIM (Sunday 18.12.2011 at 14:15 - 16:20) 72 ES08: OPTIMAL DESIGN(Room: Bloomsbury) . . . 72
ES12: HIGH-DIMENSIONAL STATISTICS,SPARSITY AND APPLICATIONS(Room: Jessel) . . . 72
ES22: DEALING WITH RARE EVENTS:RESAMPLING-BASED METHODS(Room: S261) . . . 73
ES26: MCMCFOR ESTIMATING DIFFUSIONS(Room: Gordon) . . . 74
ES35: LONGITUDINAL DATA ANALYSIS(Room: Woburn) . . . 74
ES40: BAYESIAN NONPARAMETRICS MODELLING(Room: Senate) . . . 75
ES46: RECENT ADVANCES IN MULTI-STATE MODELS(Room: Bedford) . . . 76
ES49: NEW DEVELOPMENTS IN QUANTILE REGRESSION(Room: Torrington) . . . 77
ES56: APPROACHES TO THE TREATMENT OF IMPRECISION OF STATISTICAL DATA(Room: Court) . . . 78
ES72: NETWORKING ONBIOSTATISTICS:THEBIOSTATNETPROJECTIII (Room: S264) . . . 78
EP03: POSTERS SESSIONIII (Room: Chancellor’s) . . . 79
Parallel Session K – CFE (Sunday 18.12.2011 at 16:50 - 18:30) 81 CS01: MODELLING MULTIVARIATE FINANCIAL TIME SERIES(Room: Bloomsbury) . . . 81
CS24: BAYESIAN EMPIRICAL MACROECONOMICS(Room: Woburn) . . . 81
CS21: MACRO-FINANCE INTERFACE(Room: Jessel) . . . 82
CS44: VOLATILITY ESTIMATION AND FORECASTING(Room: Court) . . . 82
CS59: VOLATILITY,HEAVY TAILS AND RISK(Room: Torrington) . . . 83
CS65: FINANCIAL MARKETS CONTAGION(Room: Gordon) . . . 84
CS70: EVALUATING FINANCIAL PERFORMANCES(Room: Bedford) . . . 84
Parallel Session K – ERCIM (Sunday 18.12.2011 at 16:50 - 18:30) 86 ESI04: SPACE-TIME MODELLING IN DISEASE MAPPING(Room: Senate) . . . 86
ES13: ROBUST METHODS FOR FINANCIAL APPLICATIONS(Room: S264) . . . 86
ES32: DIAGNOSTIC TESTS FOR INDEPENDENT AND TIME-SERIES DATA(Room: S261) . . . 87
ES44: MODELLING THE EXTREMES(Room: G16) . . . 87
ES45: MIXTURE MODELS:THEORY AND DATA ANALYSIS(Room: B36) . . . 88
Sunday 18.12.2011 11:05 - 12:45 CFE-ERCIM 2011 Parallel Session I – ERCIM penalty. We considered a variety of scenarios including cases where 2p» n. Finally, we analyse some data, report our findings and discuss the key issues.
E443: Optimal choice of reference subclass in categorical regression models
Presenter: Defen Peng, University of Limerick, Ireland
Co-authors: Gilbert MacKenzie
Issues surrounding the choice of reference subclasses for categorical variables in parametric regression models are addressed. We focus on: (a) techniques which maximize the precision of the resulting estimators, (b) discrepancy from the ideal allocation and (c) multi-collinearity. First, we derive the optimal design allocation and provide a statistic, based on generalized variance and its distribution for measuring the discrepancy between the optimal allocation and the observed allocations occurring in observational studies. Next we focus on reference category subclass choice, obtaining general expressions for the variance-covariance matrix of the estimators in linear and other generalized linear models for a single categorical variable. Then we extend the investigation to multiple categorical variables by means of simulation studies, wherein we demonstrate the use of the techniques. Later we address the issue of multi-collinearity which is especially important when the regression model contains multiple categorical variables with sparse subclasses. In this case the model design matrix may become unstable or even less than full rank. To begin, we consider the case of linear model with a single categorical variable and rapidly extend our investigation into GLMs with multiple categorical variables and end by drawing some general conclusions about the various methods developed.
ES33 Room S261 ROBUST METHODS IN SMALL AREA ESTIMATION Chair: Isabel Molina
E116: Controlling the bias of robust small area predictors
Presenter: David Haziza, University of Montreal, Canada
Co-authors: Valery Dongmo Jiongo, Pierre Duchesne
The user demand for small area estimators has been growing in most countries. This led survey statisticians to develop theoretically sound and yet practical estimation procedures, providing reliable estimators for small areas. A popular estimation method is the so-called empirical best linear unbiased prediction (EBLUP). However, the EBLUP is sensitive to the presence of outliers. That is, including or excluding outlying units from its computation may have a large impact on its magnitude. In recent years, the problem of robust small area estimation has received considerable interest. We introduce two new robust small area estimators that are robust in the presence of outliers. Results of a simulation study that compares the performance of several robust estimators in terms of relative bias and relative efficiency will be presented.
E425: On computing and tuning some simple and robust unit-level SAE estimators
Presenter: Tobias Schoch, University of Applied Sciences Northwestern Switzerland, Switzerland
Co-authors: Beat Hulliger
The need for robust estimates of area-level characteristics is of increasing importance for production in Official Statistics (OS), since of EBLUP methods are vulnerable to outliers. Statisticians in OS often fit mixed linear models (MLM) and use EBLUP methods for Small Area Estimation (SAE). The robust EBLUP methods, e.g. M-estimator of an MLM’s core parameters (called RML), as well as the robust prediction of random effects and area means, however, require more attention to the (non-trivial) issue of robustness-tuning and to computational aspects in general. In the context of an RML fitting exercise, users typically attempt to refer to the well-known robustness properties of simple location-scale models (such as the fact that the M-estimator of location with Huber psi-function obtains a relative asymptotic efficiency of approx. 95% w.r.t. maximum likelihood estimator at the true model if the tuning constant is 1.345). However, these properties do not always directly carry over to the RML method. As a consequence, the choice of how best to tune RML-estimators is much more involved. A simulation study of the computational aspects (incl. breakdown-point considerations) of robust estimators for unit-level SAE models is carried out and recommendations concerning the choice of robustness tuning constants are made.
E466: Robustness analysis of unbalanced linear mixed modeling
Presenter: Roland Fried, TU Dortmund University, Germany
Co-authors: Isabel Molina Peralta, Betsabe Perez Garrido, Anita Thieler
Robust approaches for fitting linear mixed models are investigated and compared. Starting from the simplest situation of fitting a constant mean µ in the presence of independent random group effects uiand independent random errors ei j, Yi j= µ + ui+ ei j, with i = 1, . . . , I being the group index and j = 1, . . . , nidenoting the individuals, we subsequently elaborate more complicated models with covariates. We start from the simple model given above, since methods which do not work there will hardly be useful in more complex situations. While balanced scenarios with equal group sizes n1= . . . = nIhave been analyzed frequently in the past, unbalanced designs have been given less attention so far. In small area estimation we are usually confronted with unbalanced designs and want to protect against the possibility of outlying individuals or groups. Our analysis aims at identifying which methods are useful under which scenarios and under which type of contamination.
E712: Outlier robust domain estimation for business survey data
Presenter: Nikos Tzavidis, University of Southampton, United Kingdom
Co-authors: Sabine Krieg, Marc Smeets, Chiara Bocci, Virginie Blaess
Outliers are a fact of life for any survey, and especially so for business surveys. If outliers are a concern for estimation of population quantities, it is safe to say that they are even more of a concern in small area estimation, where sample sizes are considerably smaller and model-dependent estimation is the norm. It is therefore of interest to see how outlier robust survey estimation can be adapted to this situation. The development of outlier robust small area methodologies has been the focus of recent small area literature. We review a range of outlier robust small area methodologies and apply these to business survey data from the Netherlands. We discuss both point and Mean Squared Error estimation (MSE) using analytic and bootstrap-type MSE estimators. Finally, we place some emphasis on providing practical guidelines to the survey practitioner for working with outlier robust small area methodologies.
ES36 Room Jessel PARAMETRIC AND SEMIPARAMETRIC HAZARDS MODELS AND ANALYSES Chair: M. Carmen Pardo
E069: Inference for a semiparametric generalized logit-based proportional hazards model in survival analysis
Presenter: Martha Avendano, Complutense University of Madrid, Spain
Co-authors: Maria del Carmen Pardo, Narayanaswamy Balakrishnan
A generalized logit-based proportional hazards model of survival analysis is introduced as an alternative to the popular Cox proportional-hazards model. Our model is based on a generalization of the logistic distribution and it is semiparametric, since it can be factored into a parametric part consisting of a regression parameter vector associated with the covariates and a non-parametric part that can be left completely unspecified. The partial loglikelihood approach can be applied to estimate the model parameters. The asymptotic properties of the parameter estimates are established. We compare our models with the Cox proportional hazards model by using one example.
