职业学校学生学习心理之浅谈

职业学校学生的学习心理在学习动机、学习兴趣、学习能力等方面有其特殊性。职业教育应合理构筑教 学计划、强化专业技能培训,提倡“以学为中心”培养学生学习兴趣,帮助、辅导学习有困难的学生。     

货币政策影响居民储蓄行为的有效性分析

本选取了我国1994年至2000年的统计数据，通过统计学的方法来分析货币政策在分流我国居民的巨额储蓄时是否有效，并进一步分析了对货币政策效果产生干扰的制度因素。     

FDI对中国经济发展的绩效及对策分析

改革开放以来,中国FDI流入量不断提高,对经济发展的作用也日益重要。今后,应当根据全球FDI流动的新趋势,加快我国吸收外FDI方式创新。     

基于岗位胜任力的某通信服务分公司的中干培训体系构建

为适应公司数字化转型,某通信服务分公司在2019年调整了中干组织结构。调整1年后,高层发现很多中干急缺管理、专业知识等相关知识和技能,这就需要公司构建培训体系来提高中干的整体能力和素质来适应快速的转型。论文通过问卷调研、统计分析、专家咨询法等方法构建了适应通信服务公司转型的中干培训体系,试图为构建完整的通信服务公司中层管理者胜任能力模型评价体系奠定基础。     

探究“新零销售”模式下影响销售量的相关因素

在新零销售模式的推动下,客户越来越倾向于消费个性化的商品。为了精准预测客户的消费需求,论文以单款单色产品为研究对象,通过建立多元回归分析的数学模型,来探究影响商品销售量的相关因素。经过分析发现,除了一些定性因素外,定量因素对销售量也产生了一定的影响,其中实际花费总金额、实际销售单价、库存数等定量因素对销售量的影响较大,且各个变量之间具有相关性,所以电商平台应特别关注这三个变量的影响。     

Prediction of the Indian summer monsoon using a stacked autoencoder and ensemble regression model

The study of climatic variables that govern the Indian summer monsoon has been widely explored. In this work, we use a non-linear deep learning-based feature reduction scheme for the discovery of skilful predictors for monsoon rainfall with climatic variables from various regions of the globe. We use a stacked autoencoder network along with two advanced machine learning techniques to forecast the Indian summer monsoon. We show that the predictors such as the sea surface temperature and zonal wind can predict the Indian summer monsoon one month ahead, whereas the sea level pressure can predict ten months before the season. Further, we also show that the predictors derived from a combination of climatic variables can outperform the predictors derived from an individual variable. The stacked autoencoder model with combined predictors of sea surface temperature and sea level pressure can predict the monsoon (June-September) two months ahead with a 2.8% error. The accuracy of the identified predictors is found to be superior to the state-of-the-art predictions of the Indian monsoon.     

Public-private sector wage differentials in Germany: Evidence from quantile regression

This paper measures and decomposes the differences in earnings distributions between public sector and private sector employees in Germany for the years 1984–2001. Oaxaca decomposition results suggest that conditional wages are higher in the public sector for women but lower for men. Using the quantile regression decomposition technique proposed by Machado and Mata (2004), we find that the conditional distribution of wages is more compressed in the public sector. At the low end of wages, differences in characteristics explain less than the raw wage gap when it is the opposite at high wages. Separate analyses by work experience and educational groups reveal that the most experienced employees and those with basic schooling do best in the public sector. All these results are stable over the 80s and 90s.I thank Michael Lechner and the Centre for European Economic Research (ZEW), Mannheim, for letting me work with the full sample of the German Socio-Economic Panel (GSOEP). I am grateful to an anonymous referee and particularly to the editor, Bernd Fitzenberger, for providing me detail comments and suggestions that have significantly improved the paper. I have also benefited from discussions with Michael Lechner and Ruth Miquel.First version received: April 2002/Final version received: June 2004     

Does Gibrat's Law hold among young,small firms?

According to Gibrat's Law of Proportionate Effect, the growth rate of a given firm is independent of its size at the beginning of the examined period. Aimed at extending this line of investigation, the present paper uses quantile regression techniques to test whether Gibrat's Law holds for new entrants in a given industry: that is for new small firms in the early stage of their life cycle. The main finding is that for some selected industries in Italian manufacturing Gibrat's Law fails to hold in the years immediately following start-up, when smaller firms have to rush in order to achieve a size large enough to enhance their likelihood of survival. Conversely, in subsequent years the patterns of growth of new smaller firms do not differ significantly from those of larger entrants, and the Law therefore cannot be rejected.JEL Classification: L11, L60Previous versions of this paper were presented at the 27th Annual EARIE Conference (Lausanne, 7-10 September 2000) and at seminars held between 2000 and 2003 at the Economics Department of Harvard University, the Catholic University of Milan, the University of Ferrara, the University of Bologna, the Bank of Italy, and Athens University of Economics & Business. We would like to thank Carlo Bianchi, Giuseppe Colangelo, Giovanni Dosi, Steven Klepper (Editor), Stephen Martin, Ariel Pakes, Aman Ullah and, in particular, Helen Louri and two anonymous referees for their valuable comments on earlier drafts. Financial support from MIUR (Year 2000; protocol #MM13038538_001; project leader: E. Santarelli) is gratefully acknowledged.Correspondence to: E. Santarelly     

Distributional assumptions and a test of the dual labor market hypothesis

Recent application of the switching regression model to allocate workers into the primary and secondary labor markets is considered to be the best solution to the classification problem of the empirical testing of the dual labor market theory. In such models, normality of the error terms is assumed. This paper adopts the switching regression model to test the dual labor market theory by assuming different distributions of the error terms. The test results strongly support the dual labor market theory regardless of the assumption one makes about the error terms. However, the results indicate that different distribution can lead to different percentage distributions of workers in the two segments. In particular, the normal distribution generates more workers in the primary segment than the non-normal distributions. Therefore, care must be taken not to generalize the type of industries or occupations that fall under the primary and secondary segments. First version received: October 2000/Final version received: March 2002 RID="*" ID="*"  I would like to thank Kevin Lang, Robert Marshall, and two anonymous referees for their helpful comments. I am grateful for comments received from the session participants of the Western Economic Association International Conference, San Francisco, CA, June 28–July 2, 1996, and the Midwest Economic Association Conference, Kansas City, 1997. I thank George Bonney, the Chief Statistician of Fox Chase Cancer Center, Philadelphia for his comments. Any remaining errors are my responsibility. I gratefully acknowledge financial support from Penn State Research and Development Grant, 1995.     

第二产业主导产业的选择与实证分析

中国目前仍处于传统工业化中期,今后的任务是推进工业现代化,向发达经济阶段迈进.第二产业主导产业的发展在整个经济发展中处于举足轻重的地位,因此,第二产业主导产业的选择是加速发展第二产业的关键,正确确定目前第二产业中的主导产业,对中国第二产业以及整个国民经济的发展意义重大.本文以第二产业主导产业选择的基准为依据,运用主成份分析法,确定了2000-2050年各阶段第二产业的主导产业.