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妈妈背患病儿子一路读到清华博士究竟什么情况？妈妈背患病儿子一路读到清华博士时间过程详解( 六 )


Empirical Research on Financing Constraints and Investment Behavior of Chinese Listed Companies: China\'s financial system is undergoing reform and innovation in the financial system during the transition period. However, the marginal capital-output rate has risen sharply in recent years, indicating that China\'s capital or financial efficiency is deteriorating. We use a VAR model to analyze the impacts fundamental factors of marginal productivity on Tobin Q with a panel data of listed companies of China from 1997-2008. The results show that the availability of internal funds is more important in explaining investment as well as the evidence of inefficient allocation of capital and that slower growth rates in China. This paper provides microscope insights for further deepening reform of China\'s financial system.
中国上市公司融资约束与投资行为的实证研究：转型期中国金融体系正在进行金融体制改革与创新 。 然而 ， 近年来边际资本产出率急剧上升 ， 表明中国的资本或金融效率正在恶化 。 我们使用VAR模型分析了托宾Q边际生产力的影响因素 ， 以及1997 - 2008年中国上市公司的面板数据 。 结果表明 ， 内部资金的可用性对于解释投资以及资本配置效率低下和中国经济增长放缓的证据更为重要 。 本文为进一步深化中国金融体系改革了显微镜见解 。
Besides, the projects K given trained my programming, data analysis, and logical thinking skills. In project 1, we explored the upward mobility through data analysis and data visualization. We found the pooling average upward mobility of San Francisco (38405.54) is higher than the atlas\'s (34311.68) and children from low-income families do well generally have better outcomes for those from high-income families. In project 2, we checked whether the class size has an impact on students score by data plot with threshold and regression analysis. In project 3, we conducted an empirical project to analyze data from pilot studies conducted in partnership with the King County Public Housing Authority (KCHA) and the Seattle Public Housing Authority (SHA). Families willing to accept the severs from CMTO are randomly divided into the control group and experimental treatment group. The results showed that being randomly assigned to receive the treatment increases the probability of moving to a high opportunity neighborhood by 18 percentage points while treatment on the treated increases by 23%. There is evidence of treatment effect heterogeneity by the Public Housing Authority, and lack of evidence of treatment effect heterogeneity by family income. In project 4, we use variables from Google DataCommons to predict intergenerational mobility using machine learning methods. The measure of intergenerational mobility that we will focus on is the mean rank of a child whose parents were at the 25th percentile of the national income distribution in each county (kfr_pooled_p25). My goal is to construct the best predictions of this outcome using other variables, an important step in creating forecasts of upward mobility that could be used for future generations before data on their outcomes become available. We found Random Forest performed the best in this dataset. Random Forest has many strengths: on many current datasets, it has great advantages over other algorithms and performs well; it can handle very high dimensional (feature a lot) data, and no need to make feature selection (Feature subsets are randomly selected); After the training, it can give which features are more important; When creating a random forest, the generalization error without bias estimation and the model generalization ability is strong.; Training speed is fast, easy to make parallelization method; In the training process, can detect the interaction between the features; The implementation is relatively simple; For unbalanced data sets, it balances errors; If a large part of the features is lost, accuracy can still be maintained. However, Random forests have been proven to be over-specified on some noisy classifications or regression problems; For data with different values, attributes with more values will have a greater impact on random forests, so the attribute weights generated by random forests on such data are not credible. Therefore, we need to test it on other latest upgraded models (i.e. LightGBM or deep neural network), and we can also propose a novel to improve the performance.


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