能源价格波动对中国物价水平的潜在与实际影响

目的/意义原油作为重要的工业原料、大宗商品和战略储备物资,其价格波动对全球经济发展具有重大影响,对原油价格进行预测是能源经济领域的热点研究课题。对国际原油价格预测的主要方法进行系统梳理并展望未来研究方向,有助于该研究领域的纵深发展。设计/方法基于295篇国际期刊文献,对国际油价预测的发展阶段、发表期刊、研究机构等进行归纳总结,然后对近二十年油价预测领域的主要研究方法进行系统梳理和分析,最后对油价预测工作进行评述和趋势展望。结论/发现自2008年金融危机后,油价预测研究快速发展,相关文章主要发表于能源经济领域的权威期刊《Energy Economic》。现有油价预测方法主要包括计量模型、机器学习和混合预测模型。展望未来,利用高频日内交易数据,融合计量和机器学习前沿方法,对原油价格进行区间预测或概率密度预测是值得探索的方向,此外还应深化油价预测结果的实际应用价值。     

我国“放管服”改革成效评估与展望

目的/意义 近年来,“放管服”改革已成为学术界研究的热点主题,十九大的胜利召开对“放管服”改革也提出了新的要求和指导建议。因此,系统梳理和比较国内“放管服”改革研究状况,对今后“放管服”改革的理论与实践推进具有重要意义。 设计/方法 选取国内“放管服”改革研究的重要期刊文献,对比分析十九大召开前后国内“放管服”改革研究的整体发展现状及研究内容差异。 结论/发现 十九大召开前后“放管服”改革的研究热点主题存在“差异多、共性少”的特点,而研究团队则表现为“共性多、差异少”,研究内容则呈现出“大同小异”的高度一致性特点。从内涵和问题优化上看,前后阶段表现为“共性少、差异多”;而从实践模式及效果评价上看,前后阶段表现为继承与发扬的关系,后阶段研究是对前期研究的持续深入推进。最后,从强化研究团队间的沟通合作,深入推进案例和实证研究,增加定性与定量相结合的混合研究三个方面提出未来研究展望。     

元认知与大学英语网络自主学习

目前我们汉译英的课堂教学模式过于简单且效率低下,本文提出了一种针对低学业成就者的、基于学习策略培训的汉译英SB I教学模式,从汉语语义结构分析、英语句子结构定型、英语词汇选择以及检查核对等多方面尝试将汉译英学习策略的培训渗透到汉译英的课堂教学活动中,以期提高这些学习者的汉译英技能,帮助他们掌握必要的语言学习策略,培养他们的自主学习能力,改善课堂教学效果。     

金融资产收益跳跃行为的微观机制

文章发展了一个均衡模型,从微观机理角度解释了为什么资产价格会发生不频繁的跳跃行为.在该模型中,投资者学习不可直接观测的、真实的经济系统状态并为此支付成本的行为诱发了资产价格的跳跃行为.而投资者是否选择学习的行为则取决于投资者的偏好参数和收入的条件波动率.     

主族控制下的族群杂居村落:权力的文化网络视角

本文借助“权力的文化网络”视角,深层再现云南省一个少数民族山区--蚌岚河槽（村庄）传统主族控制和毕摩操控的村落政治格局。作者认为,1949年以前槽区的社会政治局面是主族控制和毕摩操控,主族及其首领毕摩依靠乡村文化网络获得权威和认受性,他们在权力的文化网络中发挥着组织领导的作用,凭借对乡村文化网络（族群内部的宗族关系和宗教信仰以及族群关系等）的操控,主族及其首领毕摩有力地控制着乡村社会。     

Feature selection and machine learning with mass spectrometry data for distinguishing cancer and non-cancer samples

This is a comparative study of various clustering and classification algorithms as applied to differentiate cancer and non-cancer protein samples using mass spectrometry data. Our study demonstrates the usefulness of a feature selection step prior to applying a machine learning tool. A natural and common choice of a feature selection tool is the collection of marginal p-values obtained from t-tests for testing the intensity differences at each m/z ratio in the cancer versus non-cancer samples. We study the effect of selecting a cutoff in terms of the overall Type 1 error rate control on the performance of the clustering and classification algorithms using the significant features. For the classification problem, we also considered m/z selection using the importance measures computed by the Random Forest algorithm of Breiman. Using a data set of proteomic analysis of serum from ovarian cancer patients and serum from cancer-free individuals in the Food and Drug Administration and National Cancer Institute Clinical Proteomics Database, we undertake a comparative study of the net effect of the machine learning algorithm–feature selection tool–cutoff criteria combination on the performance as measured by an appropriate error rate measure.     

Precedence tests and Lehmann alternatives

G = F ^k (k > 1); G = 1 − (1−F) ^k (k < 1); G = F ^k (k < 1); and G = 1 − (1−F) ^k (k > 1), where F and G are two continuous cumulative distribution functions. If an optimal precedence test (one with the maximal power) is determined for one of these four classes, the optimal tests for the other classes of alternatives can be derived. Application of this is given using the results of Lin and Sukhatme (1992) who derived the best precedence test for testing the null hypothesis that the lifetimes of two types of items on test have the same distibution. The test has maximum power for fixed κ in the class of alternatives G = 1 − (1−F) ^k , with k < 1. Best precedence tests for the other three classes of Lehmann-type alternatives are derived using their results. Finally, a comparison of precedence tests with Wilcoxon's two-sample test is presented. Received: February 22, 1999; revised version: June 7, 2000     

Bootstrap tests: how many bootstraps?

In practice, bootstrap tests must use a finite number of bootstrap samples. This means that the outcome of the test will depend on the sequence of random numbers used to generate the bootstrap samples, and it necessarily results in some loss of power. We examine the extent of this power loss and propose a simple pretest procedure for choosing the number of bootstrap samples so as to minimize experimental randomness. Simulation experiments suggest that this procedure will work very well in practice.     

Diagnostic checks for discrete data regression models using posterior predictive simulations

Model checking with discrete data regressions can be difficult because the usual methods such as residual plots have complicated reference distributions that depend on the parameters in the model. Posterior predictive checks have been proposed as a Bayesian way to average the results of goodness-of-fit tests in the presence of uncertainty in estimation of the parameters. We try this approach using a variety of discrepancy variables for generalized linear models fitted to a historical data set on behavioural learning. We then discuss the general applicability of our findings in the context of a recent applied example on which we have worked. We find that the following discrepancy variables work well, in the sense of being easy to interpret and sensitive to important model failures: structured displays of the entire data set, general discrepancy variables based on plots of binned or smoothed residuals versus predictors and specific discrepancy variables created on the basis of the particular concerns arising in an application. Plots of binned residuals are especially easy to use because their predictive distributions under the model are sufficiently simple that model checks can often be made implicitly. The following discrepancy variables did not work well: scatterplots of latent residuals defined from an underlying continuous model and quantile–quantile plots of these residuals.     

Testing for No Effect by Cosine Series Methods

The properties of three lack-of-fit tests that are related to non-parametric cosine regression analysis are examined in the context of testing for a constant mean function. Analytic power comparisons of these tests vs a most powerful test are made using intermediate asymptotic relative efficiency. In particular, a data-driven test is produced which is asymptotically as efficient as the most powerful test over a class of alternatives. A small scale simulation experiment is conducted to ascertain the extent that the large sample comparisons are applicable to finite samples.