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Event(s) on April 2008


  • Tuesday, 8th April, 2008

    Title: Dynamic Semiparametric Factor Modelling with Applications in Energy Pricing, Medicine and Finance
    Speaker: Prof. Wolfgang Haerdle, Humboldt University, Germany
    Time/Place: 11:00  -  11:45
    FSC 1217
    Abstract: High-dimensional regression problems which reveal dynamic behavior are typically analyzed by time propagation of a few number of factors. The inference on the whole system is then based on the low-dimensional time series analysis. Such high-dimensional problems occur frequently in many different fields of science. In this paper we address the problem of inference when the factors and factor loadings are estimated by semiparametric methods. This more flexible modelling approach poses an important question: Is it justified, from inferential point of view, to base statistical inference on the estimated times series factors? We show that the difference of the inference based on the estimated time series and 'true' unobserved time series is asymptotically negligible. Our results justify fitting vector autoregressive processes to the estimated factors, which allows one to study the dynamics of the whole high-dimensional system with a low-dimensional representation. We illustrate the theory with a simulation study. Also, we apply the method to a study of the dynamic behavior of implied volatilities and discuss other possible applications in finanace and economics.


  • Tuesday, 8th April, 2008

    Title: The Stochastic Fluctuation of the Quantile Regression Curve
    Speaker: Mr. Song Song, Humboldt University, Germany
    Time/Place: 11:45  -  12:30
    FSC 1217
    Abstract: Let (X1,Y1), ..., (Xn,Yn) be i.i.d. rvs and let l(x) be the unknown p-quantile regression curve of Y on X. A quantile-smoother ln(x) is a localized, nonlinear estimator of l(x). In many applications it is necessary to know the stochastic fluctuation of the process {ln(x)−l(x)}. Using strong approximations of the empirical process and extreme value theory allows us to consider the asymptotic maximal deviation sup_{0<=x<=1}|ln(x)−l(x)|. The derived result helps in the construction of a uniform confidence band for the quantile curve l(x). This confidence band can be applied as a model check, e.g. in econometrics. The strong uniform consistency rate is also established under general conditions. An application considers a labor market discrimination effect.

 

 


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