Robust estimation of dimension reduction space

Most dimension reduction methods based on nonparametric smoothing are highly sensitive to outliers and to data coming from heavy-tailed distributions. We show that the recently proposed methods by Xia et al. (2002) can be made robust in such a way that preserves all advantages of the original approach. Their extension based on the local one-step M-estimators is su±ciently robust to outliers and data from heavy tailed distributions, it is relatively easy to implement, and surprisingly, it performs as well as the original methods when applied to normally distributed data.Dimension reduction, Nonparametric regression, M-estimation

Robust Estimation of Dimension Reduction Space

Most dimension reduction methods based on nonparametric smoothing are highly sensitive to outliers and to data coming from heavy-tailed distributions.We show that the recently proposed methods by Xia et al.(2002) can be made robust in such a way that preserves all advantages of the original approach.Their extension based on the local one-step M-estimators is sufficiently robust to outliers and data from heavy tailed distributions, it is relatively easy to implement, and surprisingly, it performs as well as the original methods when applied to normally distributed data.Dimension reduction;Nonparametric regression;M-estimation

Yxilon – a Modular Open-Source Statistical Programming Language

Statistical research has always been at the edge of available computing power. Huge datasets, e.g in DataMining or Quantitative Finance, and computationally intensive techniques, e.g. bootstrap methods, always require a little bit more computing power than is currently available. But the most popular statistical programming language R, as well as statistical programming languages like S or XploRe, are interpreted which makes them slow in computing intensive areas. The common solution is to implement these routines in low-level programming languages like C/C++ or Fortran and subsequently integrate them as dynamic linked libraries (DLL) or shared object libraries (SO) in the statistical programming language.statistical programming language, XploRe, Yxilon, Java, dynamic linked libraries, shared object libraries

Common functional component modelling

Functional data analysis (FDA) has become a popular technique in applied statistics. In particular, this methodology has received considerable attention in recent studies in empirical finance. In this talk we discuss selected topics of functional principal components analysis that are motivated by financial data.nonparametric risk management, generalized hyperbolic distribution, functional data analysis

Do Factor Shares Reflect Technology?

This note demonstrates that it is easily possible to compute technological parameters out of national income accounting data in the presence of bargaining in the labor market. Applying the method to US data, we obtain that the output elasticity with respect to capital exceed 0.5.Factor Shares, Nash Bargaining

What are the Effects of Fiscal Policy Shocks?

We propose and apply a new approach for analyzing the effects of fiscal policy using vector autoregressions. Unlike most of the previous literature this approach does not require that the contemporaneous reaction of some variables to fiscal policy shocks be set to zero or need additional information, such as the timing of wars, in order to identify fiscal policy shocks. The paper's method is a purely vector autoregressive approach which can be universally applied. The approach also has the advantages that it is able to model the effects of announcements of future changes in fiscal policy and that it is able to distinguish between the changes in fiscal variables caused by fiscal policy shocks and those caused by business cycle and monetary policy shocks. We apply the method to US quarterly data from 1955-2000 and obtain interesting results. Our key finding is that the best fiscal policy to stimulate the economy is a deficit-financed tax cut and that the long term costs of fiscal expansion through government spending are probably greater than the short term gains.Fiscal Policy, Vector Autoregression, Bayesian Econometrics, Agnostic identification

Integrable e-lements for Statistics Education

Without doubt modern education in statistics must involve practical, computer-based data analysis but the question arises whether and how computational elements should be integrated into the canon of methodological education. Should the student see and study high-level programming code right at the beginning of his or her studies? Which technology can be presented during class and which computational elements can re-occur (at increasing level of complexity) during the different courses? In this paper we address these questions and discuss where e-techniques have their limits in statistics education.electronic books, hypertext, e-supported teaching, statistical software

On Local Times of Ranked Continuous Semimartingales;Application to Portfolio Generating Functions

We derive the decomposition of the ranked continuous semimartingales i.e. order-statistics processes. We apply it to portfolios generated by functions of the ranked market weights. Thus we generalize recent results of Fernholz.Portfolio-generating function, continuous semimartingale, local time, ranked processes

A Market Basket Analysis Conducted with a Multivariate Logit Model

The following research is guided by the hypothesis that products chosen on a shopping trip in a supermarket can indicate the preference interdependencies between different products or brands. The bundle chosen on the trip can be regarded as the result of a global utility function. More specifically: the existence of such a function implies a cross-category dependence of brand choice behavior. It is hypothesized that the global utility function related to a product bundle results from the marketing-mix of the underlying brands. Several approaches exist to describe the choice of specific categories from a set of many alternatives. The models are discussed in brief; the multivariate logit approach is used to estimate a model with a German data set.market basket analysis, multivariate logit model, brand choice behavior, marketing-mix

DSFM fitting of Implied Volatility Surfaces

The implied volatility became one of the key issues in modern quantitative finance, since the plain vanilla option prices contain vital information for pricing and hedging of exotic and illiquid options. European plain vanilla options are nowadays widely traded, which results in a great amount of high-dimensional data especially on an intra day level. The data reveal a degenerated string structure. Dynamic Semiparametric Factor Models (DSFM) are tailored to handle complex, degenerated data and yield low dimensional representation of the implied volatility surface (IVS). We discuss estimation issues of the model and apply it to DAX option prices.dynamic semiparametric factor model, implied volatility, vanilla options, DAX option prices