HR and firm performance at companies in Turkey： Correlative analysis

Aim of this study is to discuss matters with HR and finn performance. Therefore, we used HR and firm performance questionnaire from Delaney and Huselid （1996）. HR variables include recruitment, training, compensation and promotion. Firm performance divided into organizational and market performance. Therefore, correlation analysis demonstrates that HR has positive and significant relationship with organizational performance, and HR has positive but weak relationship with market performance. Therefore, we can propose that HR is partly correlated with firm performance for those companies in sample. Therefore, since we didn＇t realize demography of companies in study, it is arguable, findings of this study can be generalized to other companies in Turkey.     

Analyzing yield, duration and convexity of mortgage loans under prepayment and default risks

In this article, we construct a general model, which considers the borrower’s financial and non-financial termination behavior, to derive the closed-form formula of the mortgage value for analyzing the yield, duration and convexity of the risky mortgage. Since the risks of prepayment and default are reasonably expounded in our model, our formulae are more appropriate than traditional mortgage formulae. We also analyze the effects of the prepayment penalty and partial prepayment on the yield, duration and convexity of a mortgage, and provide lenders with an upper-bound for the mortgage default insurance rate. Our model provides portfolio managers a useful framework to more appropriately appraise the mortgage and more effectively hedge their mortgage holdings. From the results of sensitivity analyses, we find that higher interest-rate, prepayment and default risks will increase the mortgage yield and reduce the duration and convexity of the mortgage.     

Statistical inference on seemingly unrelated non‐parametric regression models with serially correlated errors

This article is concerned with the inference on seemingly unrelated non‐parametric regression models with serially correlated errors. Based on an initial estimator of the mean functions, we first construct an efficient estimator of the autoregressive parameters of the errors. Then, by applying an undersmoothing technique, and taking both of the contemporaneous correlation among equations and serial correlation into account, we propose an efficient two‐stage local polynomial estimation for the unknown mean functions. It is shown that the resulting estimator has the same bias as those estimators which neglect the contemporaneous and/or serial correlation and smaller asymptotic variance. The asymptotic normality of the resulting estimator is also established. In addition, we develop a wild block bootstrap test for the goodness‐of‐fit of models. The finite sample performance of our procedures is investigated in a simulation study whose results come out very supportive, and a real data set is analysed to illustrate the usefulness of our procedures.     

Simultaneous Parameter Estimation in Exploratory Factor Analysis: An Expository Review

The classical exploratory factor analysis (EFA) finds estimates for the factor loadings matrix and the matrix of unique factor variances which give the best fit to the sample correlation matrix with respect to some goodness-of-fit criterion. Common factor scores can be obtained as a function of these estimates and the data. Alternatively to the classical EFA, the EFA model can be fitted directly to the data which yields factor loadings and common factor scores simultaneously. Recently, new algorithms were introduced for the simultaneous least squares estimation of all EFA model unknowns. The new methods are based on the numerical procedure for singular value decomposition of matrices and work equally well when the number of variables exceeds the number of observations. This paper provides an account that is intended as an expository review of methods for simultaneous parameter estimation in EFA. The methods are illustrated on Harman's five socio-economic variables data and a high-dimensional data set from genome research.     

无网格数值方法中的形函数研究

无网格法是近几年来发展的一种新的基于变分原理的数值计算方法,根据形函数构建和方程离散化方法的不同,分为不同的无网格法。文章主要讨论了移动最小二乘形函数的构建方法、性质。通过对曲线和曲面拟合算例的研究表明:权函数的选择、节点数目、影响域大小、基函数的选取等对形函数的精度和稳定性都有影响,通过数值实验给出了各参数的参考值。     

Why Isn’t More US Farmland Organic?

We develop a theoretical model to assess the dollar compensation required to induce conventional growers to convert to organic. The model incorporates the uncertainty in producers’ expectations about future returns and about the impact of policy changes on these expectations in particular. We demonstrate that a new policy which favours organic can have opposing effects on the rate of conversion. An increase in relative returns to organic today will increase conversion rates. However, if the future of the policy programme is uncertain, its introduction can increase the value of waiting to switch, which will decrease conversion rates. We then develop an empirical switching regression model that enables direct estimation of the value associated with being able to postpone the conversion decision until some of the uncertainty is resolved. The model is applied to data on organic and conventional soybeans before and after major changes in US farm policy toward organic growers. The results suggest that sunk costs associated with conversion to organic coupled with uncertainty about future returns can help to explain why there is so little organic farmland in the USA.     

最小区域法评定圆度误差数据处理的快速方法

用最小区域法评定同度误差符合最小条件.目前多采用优化法运算速度较慢,数据处理较为复杂。本文提出了一种快速方法,简单、方便、数据可靠。     

模糊——双积分并联变结构复合控制

本文提出了一种测辩模糊控制决策表的新方法——多变量线性最小二乘回归方程的计算机处理法。为克服普通模糊控制存在较大静差的缺陷,本文设计了一个模糊——双积分并联复合控制结构。该结构在克服积分饱和现象,缩短调节时间,保证系统稳定,和消除调节静差等方面十分有效。