Public investment in care services in Turkey: Promoting employment & gender inclusive growth

Recent policy debates on macroeconomic tools to counteract the prolonged global economic recession point to the potential of fiscal policies with appropriate sectoral targets for simultaneously boosting effective demand while alleviating inequalities through employment generation. This paper contributes to the debate by pointing to the social care services sector –in particular early childhood education and care (ECEC) — as an effective target of fiscal spending for robust employment generation and gender inclusive growth. We use a macro-micro simulation model to examine the aggregate and gender employment impact of increasing public expenditures on ECEC services, an underdeveloped sector in Turkey versus physical infrastructure and construction, a common target of stimulatory spending. Our methodological approach combines input-output analysis on aggregate employment effects with a statistical microsimulation approach to assess distributional outcomes. We find that an expansion of ECEC services creates not only significantly more jobs but also does so in a more gender-equitable and fiscally sustainable way than the a construction boom.     

在中文平台之间移植汉字输入法简析

具体介绍了在几种中文平台之间移植汉字输入法的方法     

Testing sphericity and intraclass covariance structures under a growth curve model in high dimension

In this article, we consider the problem of testing (a) sphericity and (b) intraclass covariance structure under a growth curve model. The maximum likelihood estimator (MLE) for the mean in a growth curve model is a weighted estimator with the inverse of the sample covariance matrix which is unstable for large p close to N and singular for p larger than N. The MLE for the covariance matrix is based on the MLE for the mean, which can be very poor for p close to N. For both structures (a) and (b), we modify the MLE for the mean to an unweighted estimator and based on this estimator we propose a new estimator for the covariance matrix. This new estimator leads to new tests for (a) and (b). We also propose two other tests for each structure, which are just based on the sample covariance matrix.

To compare the performance of all four tests we compute for each structure (a) and (b) the attained significance level and the empirical power. We show that one of the tests based on the sample covariance matrix is better than the likelihood ratio test based on the MLE.     

试论家庭生育行为经济分析的理论前提及实践意义

西方现代新家庭经济学以经济人假说为理论前提 ,进行家庭生育行为的经济分析。文章对家庭生育行为所具有自利性、理性和追求效用最大化特征进行理性剖析     

Lifetime performance index based on ranked set sampling

In this article, we assume that the lifetimes of products follow a one-parameter exponential distribution and use both conjugate and Jeffreys prior distributions for finding a Bayes estimator based on the RSS scheme for lifetime performance index CL. We also obtained maximum likelihood and adhac estimators for CL. Monte Carlo simulation study is done for comparing the obtained estimators in two sampling schemes SRS and RSS. Fisher information has been utilized to obtain lower confidence bound of CL for both schemes in asymptotically state and use this bounds for hypothesis testing of CL.     

The effectiveness of input control based on aggregate versus bottleneck work loads

This study compares input control based on aggregate shop loads with that based on bottleneck resource loads. Two appropriate release mechanisms are developed and compared in flow and job shops that have various levels of a bottleneck constraint. Simulation results show that both the level of bottleneck and the flow pattern influence the effectiveness of the strategies. Release based on aggregate loads works well when shop loads are balanced or when there is a bottleneck but the flow is highly structured, as in a flow shop. Release based on bottleneck loads works best when flow patterns are less structured, as in a job shop.     

A Test for the Equality of Parameters for Separate Regression Models in the Presence of Heteroskedasticity

Testing for the equality of regression coefficients across two regressions is a problem considered by analysts in a variety of fields. If the variances of the errors of the two regressions are not equal, then it is known that the standard large sample F-test used to test the equality of the coefficients is compromised by the fact that its actual size can differ substantially from the stated level of significance in small samples. This article addresses this problem and borrows from the literature on the Behrens-Fisher problem to provide some simple modifications of the large sample test which allows one to better control the probability of committing a Type I error. Empirical evidence is presented which indicates that the suggested modifications provide tests which are superior to well-known alternative tests over a wide range of the parameter space.     

Empirical assessment of the applicability of shrinkage predictors in regression when past and future data differ

Jones and Copas (1986) present theoretical and simulation results on the relative merits of a Stein predictor (Copas, 1983) and the ordinary least squares predictor in the usual linear multiple regression model, when certain distributional properties of the regressor variables arising in the past differ from those for which predictions are to be made. Here, extension is made to the practical situation where the true regression parameters are unknown. A hypothesis testing procedure is developed to help determine which of shrinkage and least squares is preferable in any given instance. This approach is applied to explain some empirical evidence on the comparative merits of the two procedures, recently given by Berk (1984).     

克拉申的输入假设对外语教学的影响

语言学习包括输入、吸收和输出三个环节。输入是基础，也是语言教学的关键。本文通过分析克拉申输入假说的特点和核心要素，提出将这一假说融入到英语教学中去，结合实际情况，以学生为中心，以提高英语教学效率和学生语言运用的能力。     

Testing for lack of fit in inverse regression—with applications to biophotonic imaging

Summary.  We propose two test statistics for use in inverse regression problems Y = K θ + ɛ , where K is a given linear operator which cannot be continuously inverted. Thus, only noisy, indirect observations Y for the function θ are available. Both test statistics have a counterpart in classical hypothesis testing, where they are called the order selection test and the data-driven Neyman smooth test. We also introduce two model selection criteria which extend the classical Akaike information criterion and Bayes information criterion to inverse regression problems. In a simulation study we show that the inverse order selection and Neyman smooth tests outperform their direct counterparts in many cases. The theory is motivated by data arising in confocal fluorescence microscopy. Here, images are observed with blurring, modelled as convolution, and stochastic error at subsequent times. The aim is then to reduce the signal-to-noise ratio by averaging over the distinct images. In this context it is relevant to decide whether the images are still equal, or have changed by outside influences such as moving of the object table.