Evolution of capital structure in east Asia—corporate inertia or endeavours?

Summary.  The paper examines the capital structure adjustment dynamics of listed non-financial corporations in seven east Asian countries before, during and after the crisis of 1997–1998. Our methodology allows for speeds of adjustment to vary, not only among firms, but also over time, distinguishing between cases of sudden and smooth adjustment. Whereas, compared with firms in the least affected countries, average leverages were much higher, generalized method-of-moments analysis of the Worldscope panel data suggests that average speeds of adjustment were lower in the worst affected countries. This holds also for the severely financially distressed firms in some worst affected countries, though the trend reversed in the post-crisis period. These findings have important implications for the regulatory environment as well as access to market finance.     

Sparse partial least squares regression for simultaneous dimension reduction and variable selection

Summary.  Partial least squares regression has been an alternative to ordinary least squares for handling multicollinearity in several areas of scientific research since the 1960s. It has recently gained much attention in the analysis of high dimensional genomic data. We show that known asymptotic consistency of the partial least squares estimator for a univariate response does not hold with the very large p and small n paradigm. We derive a similar result for a multivariate response regression with partial least squares. We then propose a sparse partial least squares formulation which aims simultaneously to achieve good predictive performance and variable selection by producing sparse linear combinations of the original predictors. We provide an efficient implementation of sparse partial least squares regression and compare it with well-known variable selection and dimension reduction approaches via simulation experiments. We illustrate the practical utility of sparse partial least squares regression in a joint analysis of gene expression and genomewide binding data.     

A test for the parametric form of the variance function in a partial linear regression model

We consider the problem of testing for a parametric form of the variance function in a partial linear regression model. A new test is derived, which can detect local alternatives converging to the null hypothesis at a rate n^-1/2$n^{-1/2}$ and is based on a stochastic process of the integrated variance function. We establish weak convergence to a Gaussian process under the null hypothesis, fixed and local alternatives. In the special case of testing for homoscedasticity the limiting process is a scaled Brownian bridge. We also compare the finite sample properties with a test based on an L²$L^2$-distance, which was recently proposed by You and Chen [2005. Testing heteroscedasticity in partially linear regression models. Statist. Probab. Lett. 73, 61–70].     

Chain graph models: topological sorting of meta-arrows and efficient construction of $${\mathcal{B}}$$ -essential graphs

Essential graphs and largest chain graphs are well-established graphical representations of equivalence classes of directed acyclic graphs and chain graphs respectively, especially useful in the context of model selection. Recently, the notion of a labelled block ordering of vertices was introduced as a flexible tool for specifying subfamilies of chain graphs. In particular, both the family of directed acyclic graphs and the family of “unconstrained” chain graphs can be specified in this way, for the appropriate choice of . The family of chain graphs identified by a labelled block ordering of vertices is partitioned into equivalence classes each represented by means of a -essential graph. In this paper, we introduce a topological ordering of meta-arrows and use this concept to devise an efficient procedure for the construction of -essential graphs. In this way we also provide an efficient procedure for the construction of both largest chain graphs and essential graphs. The key feature of the proposed procedure is that every meta-arrow needs to be processed only once.     

外商直接投资影响因素的偏最小二乘回归建模分析

改革开放的20多年来,我国的外商直接投资逐年增加,对我国经济的快速发展发挥了重要的作用。而影响外商直接投资的因素有很多,不同的因素变量之间往往存在多重共线性或近似多重共线性关系,使得分析问题的难度加大。本文运用新型的多元统计数据分析方法——偏最小二乘(PLS)回归方法(既克服了传统方法的不足,又有效地消除了因素变量之间的多重共线性),分析了影响我国外商直接投资的因素,以及其影响程度,为我国进一步引进外商直接投资提供了有益的定量依据。     

Prediction theory for autoregressivemoving average processes

This paper reviews statistical prediction theory for autoregressive-moving average processes wing techniques developed in control theory. It demonstrates explicitly the connectioluns between the statistical and control theory literatures. Both the forecasting problem and the Single extraction problem am considered, udng linear least squares methods. Whereas the classical Statistical theory developed by Wiener and Kolmogomv is restricted to stationary stochaotic processes, the recursive techniques known as the Kalman filter are shown to provide a satisfactory treatment of the difference-stationary care and other more general cases. Complete results for non-invertible moving averages are also obtained.     

Estimation of regression coefficient from survey data based on test of significance

The ordinary least squares (OLS)estimator of regression coeffecient is implicitly based on I.I.D.assumption, which is rarely satisfied by survey data. Many approaches are proposed in the literature which can be classified in two broad categories as model based and design consistent.Du Mouchel and Duncan (1983) proposed a test statistic λwhich helps in testing the ignorability of sampling weights.In this article a preliminary test estimator based on λ is proposed. The model based properties of this estimator has been invetigated theoritically where as to study the design based properties simulation approach is adopted. It has been observed that the proposed estimator is a better cimpromise between model based and randomization based inferential frame work.     

A Comparative Study of Semiparametric Estimation in Partially Linear Single-index Models

We consider a semiparametric method based on partial splines for estimating the unknown function and partially linear regression parameters in partially linear single-index models. Three methods—project pursuit regression (PPR), average derivative estimation (ADE), and a boosting method—are considered for estimating the single-index parameters. Simulations revealed that PPR with partial splines was superior in estimating single-index parameters, while the boosting method with partial splines performed no better than PPR and ADE. All three methods performed similarly in estimating the partially linear regression parameters. The relative performances of the methods are also illustrated using a real-world data example.     

On Bivariate Risk Premia

This note examines the conditions under which the bivariate risk premium for one risk may be negative even if both risks are positively correlated, using a mean variance setting. The link between the bivariate risk premium and the partial bivariate risk premia is also investigated.