Prof. Anurag BANERJEE ( Durham Business School, University of Durham, U.K.)Tuesday, May 27, 2014 at 4.30 p.m. in room N305 (Cergy) The EndgameOn December 1st, 2009 President Obama announced that the U.S. troops would have started leaving Afghanistan on July 2011. Rather than simply waiting for the U.S. troops to withdraw, the Taliban forces responded to the announcement with a surge in attacks followed by a decline as the withdrawal date approached. In order to better understand these, at first, counterintuitive phenomena, this paper addresses the question of how knowing versus concealing the exact length of a strategic interaction changes the optimal equilibrium strategy by studying a two-player, zero-sum game of known and unknown duration. We show why the equilibrium dynamics is non-stationery under known duration and stationary otherwise. We show that when the duration is known the performance and effort might be increased or impaired depending on the length itself and on the nature of the interaction. We then test the model by using data available for soccer matches in the major European leagues. Most importantly, we exploit the change in rule adopted by FIFA in 1998 requiring referees to publicly disclose the length of the added time at the end of the 90 minutes of play. We study how the change in rule has affected the probability of scoring both over time and across teams' relative performance and find that the rule's change led to a 28% increase of goals during the added time.It is a joint seminar with Economics Department.Prof. Pieter KROONENBERG (Leiden University, The Netherlands) Tuesday, April 29, 2014 at 10 a.m. in room N516 (Cergy) Exploratory Analysis of Multivariate Longitudinal Data Longitudinal data are more often than not multivariate and one can therefore represent such data as a data cube of individuals × variables × time points. To analyse such data one has the choice of modelling them with stochastic models or carrying out an exploratory analysis in which one uses time for the interpretation rather than for modelling. In particular, one treats the subject mode as a fixed one just like the variables and the occasions. There are a considerable number of exploratory techniques to treat cubic data sets, but in this presentation the emphasis will be on principal component based models, but a number of associated techniques aretouched upon as well.Prof. Allan TIMMERMANN ( RADY, University of California San Diego – School of Management)Monday, March 31,2014 at 3:00 pm, EEE - La Défense, Room 220 Modeling Bond Return Predictability
Wednesday, March 19, 2014 at 12:30 pm, EEE - La Défense, Room 102 Maps Measuring Nonlinear Granger Causality in Mean
Prof. Rajendra BHANSALI (University of Liverpool)Wednesday, March 5, 2014 at 12:30 pm, EEE - La Défense, Room 203
Prof. Serge DAROLLES (Université Paris Dauphine)Wednesday, February 19, 2014 at 12:30 pm, EEE - La Défense, Room 138 Liquidity Risk Estimation in Conditional Volatility
Wednesday, February 5, 2014 at 12:30 pm, EEE - La Défense, Room 138 Improving the Efficiency of the European ETF Market: Implications for Regulators, Providers and Investors
Prof. Tolga CENESIZOGLU (HEC Montreal) Wednesday, January 22, 2014 at 12:30 pm, EEE - La Défense, Room 103 Return Decomposition over the Business Cycle
Prof. Guillaume CHEVILLON (ESSEC Business School) Wednesday, January 8, 2014 at 12:30 pm, EEE - La Défense, Room 103 Detecting and Forecasting Large Deviations and Bubbles in a Near-Explosive Random Coefficient Model
Prof. David VEREDAS (ULB , Solvay Brussels School of Economics and Management - ECARES) Thursday, November 7, 2013 at 12:30 pm, room N231 (Le Club) Systemic Risk: Liquidity Risk, Governance and Financial Stability, Forthcoming
George A. MARCOULIDES (University of California, Riverside) Thursday, February, 14th, 2013 Automated Structural Equation Modeling Strategies There is currently tremendous interest within the structural equation modeling communities on developing and applying automated strategies for the analysis of data. Some researchers refer to such automated strategies as “discovering structure in data” or “learning from data” while others simply call them “data mining”. Structural equation modeling (SEM) has for several decades enjoyed widespread popularity in a variety of areas as one of the fastest growing and dominant multivariate statistical techniques. A major reason for this popularity is that SEM permits researchers to study complex multivariate relationships among observed and latent variables whereby both direct and indirect effects can be evaluated. Although in principle researchers should fully specify and deductively hypothesize a specific model prior to data collection and testing, in practice this is often not possible, either because a theory is poorly formulated or perhaps even nonexistent. Consequently, another aspect of SEM is the exploratory mode, in which theory development can occur. Fitting a model to empirical data can be difficult, particularly when the number of variables is large. This presentation will introduce some new automated SEM strategies and elaborate on some conceptual and methodological details related to their application in a variety of settings and situations.
Tuesday, June, 19th, 2012 Optimal Forecasts in the Presence of Structural BreaksThis paper considers the problem of forecasting under continuous and discrete structural breaks and proposes weighting observations to obtain optimal forecasts in the MSFE sense. We derive optimal weights for continuous and discrete break processes. Under continuous breaks, our approach recovers exponential smoothing weights. Under discrete breaks, we provide analytical expressions for the weights in models with a single regressor and asympotically for larger models. It is shown that in these cases the value of the optimal weight is the same across observations within a given regime and differs only across regimes. In practice, where information on structural breaks is uncertain a forecasting procedure based on robust weights is proposed. Monte Carlo experiments and an empirical application to the predictive power of the yield curve analyze the performance of our approach relative to other forecasting methods.
Friday, May, 25th, 2012
Survey data is often collected in Likert scale. While purists argue that the resultant data is only ordinal, in practice, statistical tools are widely and indiscriminately used as if the data is in interval or ratio scale. In this work, we adopt a middle path, treating the data as fuzzy and examine the validity or inaccuracy of statistical inference. Of particular focus are three basic parameters, namely mean, variance and proportion, either considering a single population or while comparing them across two populations.Xavier BRY (Université de Montpellier 2)Friday, March, 16th, 2012
Wednesday, April, 20th, 2011 at 2 pm, Nautile # N305 - Cergy Campus
Eric SOUTIF (CNAM, Paris)Wednesday, December, 15th, 2010 at at 11:15 am, Nautile # N305 - Cergy Campus
Anurag Narayan BANERJEE (Durham University, UK)Wednesday, October, 13th, 2010 at 11:15 am, Nautile # N305 - Cergy Campus
Claes FORNELL (University of Michigan, USA)Thursday, September 23rd, 2010 at 11 am - Cergy Campus - seminar oganized jointly with the Department of Marketing
Gilbert SAPORTA (CNAM, Paris) October 14, 2009 at 4:30 pm - room N305 |