On the performance of the Jackknifed Liu-type estimator in linear regression model

In this paper, we are proposing a modified jackknife Liu-type estimator (MJLTE) that was created by combining the ideas underlying both the Liu-type estimator (LTE) and the jackknifed Liu-type estimator (JLTE). We will also present the necessary and sufficient conditions for superiority of the MJLTE over the LTE and JLTE, in terms of mean square error matrix criterion. Finally, a real data example and a Monte Carlo simulation are also given to illustrate theoretical results.     

A penalized spline estimator for fixed effects panel data models

Estimating nonlinear effects of continuous covariates by penalized splines is well established for regressions with cross-sectional data as well as for panel data regressions with random effects. Penalized splines are particularly advantageous since they enable both the estimation of unknown nonlinear covariate effects and inferential statements about these effects. The latter are based, for example, on simultaneous confidence bands that provide a simultaneous uncertainty assessment for the whole estimated functions. In this paper, we consider fixed effects panel data models instead of random effects specifications and develop a first-difference approach for the inclusion of penalized splines in this case. We take the resulting dependence structure into account and adapt the construction of simultaneous confidence bands accordingly. In addition, the penalized spline estimates as well as the confidence bands are also made available for derivatives of the estimated effects which are of considerable interest in many application areas. As an empirical illustration, we analyze the dynamics of life satisfaction over the life span based on data from the German Socio-Economic Panel. An open-source software implementation of our methods is available in the R package pamfe.     

Robust parameter estimation of regression model with AR(p) error terms

In this article, we consider a linear regression model with AR(p) error terms with the assumption that the error terms have a t distribution as a heavy-tailed alternative to the normal distribution. We obtain the estimators for the model parameters by using the conditional maximum likelihood (CML) method. We conduct an iteratively reweighting algorithm (IRA) to find the estimates for the parameters of interest. We provide a simulation study and three real data examples to illustrate the performance of the proposed robust estimators based on t distribution.     

A tilting algorithm for the estimation of fractional age survival probabilities

Life tables used in life insurance determine the age of death distribution only at integer ages. Therefore, actuaries make fractional age assumptions to interpolate between integer age values when they have to value payments that are not restricted to integer ages. Traditional fractional age assumptions as well as the fractional independence assumption are easy to apply but result in a non-intuitive overall shape of the force of mortality. Other approaches proposed either require expensive optimization procedures or produce many discontinuities. We suggest a new, computationally inexpensive algorithm to select the parameters within the LFM-family introduced by Jones and Mereu (Insur Math Econ 27:261–276, 2000). In contrast to previously suggested methods, our algorithm enforces a monotone force of mortality between integer ages if the mortality rates are monotone and keeps the number of discontinuities small.     

Unternehmenserfolg, Wechsel im Vorstandsvorsitz und Disziplinierung von Finanzvorständen

The Chief Financial Officer (CFO) is often referred to as a company’s No. 2 on contemporary management boards; yet corresponding empirical evidence is scarce. As a result, academic literature has not yet investigated whether CFOs—like CEOs—are dismissed more frequently if corporate performance is poor and to what extent forced Chief Executive Officer (CEO) turnover also influences disciplinary action towards CFOs. Therefore, in this paper we examine the antecedents of forced CFO departures in the largest German corporations between 1999 and 2006. Building on principal-agent theory, we expect respective relationships between the board of directors, the CEO, and the CFO. Moreover, we propose that principals also take team-specific and firm-specific human capital into consideration when disciplining agents. We find that poor corporate performance and forced CEO turnover both independently increase the likelihood of CFO dismissal. In addition, we find indications for a close team relationship between CFOs and CEOs. In summary, our results support the prominent role of CFOs alongside CEOs on contemporary management boards and suggest a more detailed consideration of CFOs in future research on performance consequences of managerial successions.     

Two-stage Liu estimator in a simultaneous equations model

Two-stage least squares estimation in a simultaneous equations model has several desirable properties under the problem of multicollinearity. So, various kinds of improved estimation techniques can be developed to deal with the problem of multicollinearity. One of them is ridge regression estimation that can be applied at both stages and defined in Vinod and Ullah [Recent advances in regression methods. New York: Marcel Dekker; 1981]. We propose three different kinds of Liu estimators that are named by their implementation stages. Mean square errors are derived to compare the performances of the mentioned estimators and two different choices of the biasing parameter are offered. Moreover, a numerical example is given with a data analysis based on the Klein Model I and a Monte Carlo experiment is conducted.     

创意设计的发展走向与核心竞争力

"创意设计"具有精神文化和物质文化双重属性,它既是文化创意产业的重要组成部分,又是工业产品、人居环境、营商、沟通等中间服务环节,更是工业产业创新的重要来源;文化创意语境下的设计业应该是"两种文化"紧密结合的产物,它的工具性和本体性形成了现代城市文化中设计的两种形态;创意设计把创意与创新作为自己的立足之本,建立由设计实践与设计理论共同构筑的设计生态系统,在深入挖掘中华民族优秀文化精神内核的基础上,实现高新技术与文化创意的深度融合,培育鲜明特色的城市文化,促进产业的转型和优化升级,铸造创意设计的核心竞争力.     

Lottery pricing under time pressure

This article investigates how subjects determine minimum selling prices for lotteries. We design an experiment where subjects have at every moment an incentive to state their minimum selling price and to adjust the price, if they believe that the price that they stated initially was not optimal. We observe frequent and sizeable price adjustments. We find that random pricing models cannot explain the observed price patterns. We show that earlier prices contain information about future price adjustments. We propose a model of Stochastic Pricing that offers an intuitive explanation for these price adjustment patterns.     

Financing social security out of contributions: About origins,present discussions and prospects of a success story

In writing the overture to an issue on contribution financed social security one cannot but speak of Bismarck; it must also address Beveridge who saw contributions, although in their design and role clearly differently from Bismarck, as one core revenue tool to finance his vision. Beveridge attributed to the private financial sector a prominent role in securing people against the negative effects on income of shocks and crises, while Bismarck did not. Beveridge's concept, when first published, had, and still has today, the most attractive charm of rigorously satisfying peoples' striving for equitable and inclusive societal solutions. Bismarck's concept intrinsically offers income security only to those who contribute, while the level of protection depends on the level of contributions paid (with the exception of health insurance). In reality, both concepts, where implemented, had to face the realities of socio‐economic and political developments: Beveridge's vision was achieved in respect of access to health services where his proposal, in its predominantly tax‐financed version, has since turned into a worldwide blueprint for health schemes; in its other components, it was not resilient enough to achieve the intended standards and now is replete with means‐tested (poor relief) elements. Bismarck's scheme has proven its potential to achieve “universality”, not necessarily by theoretical design but as a matter of fact, i.e. covering people from cradle to grave (like Beveridgean schemes). With globalization, schemes of both origins have had to face massive neoliberal attacks over the last three decades. Which of the approaches is best able to survive must be left an open question: in the current worldwide context of rapid change, both have weak and strong points, and whether a symbiosis of the two offers the answer to future challenges remains to be seen.     

Estimating the density of a possibly missing response variable in nonlinear regression

This paper considers linear and nonlinear regression with a response variable that is allowed to be “missing at random”. The only structural assumptions on the distribution of the variables are that the errors have mean zero and are independent of the covariates. The independence assumption is important. It enables us to construct an estimator for the response density that uses all the observed data, in contrast to the usual local smoothing techniques, and which therefore permits a faster rate of convergence. The idea is to write the response density as a convolution integral which can be estimated by an empirical version, with a weighted residual-based kernel estimator plugged in for the error density. For an appropriate class of regression functions, and a suitably chosen bandwidth, this estimator is consistent and converges with the optimal parametric rate n^1/2. Moreover, the estimator is proved to be efficient (in the sense of Hájek and Le Cam) if an efficient estimator is used for the regression parameter.