Quantile regression for robust estimation and variable selection in partially linear varying-coefficient models

In this paper, we develop a new estimation procedure based on quantile regression for semiparametric partially linear varying-coefficient models. The proposed estimation approach is empirically shown to be much more efficient than the popular least squares estimation method for non-normal error distributions, and almost not lose any efficiency for normal errors. Asymptotic normalities of the proposed estimators for both the parametric and nonparametric parts are established. To achieve sparsity when there exist irrelevant variables in the model, two variable selection procedures based on adaptive penalty are developed to select important parametric covariates as well as significant nonparametric functions. Moreover, both these two variable selection procedures are demonstrated to enjoy the oracle property under some regularity conditions. Some Monte Carlo simulations are conducted to assess the finite sample performance of the proposed estimators, and a real-data example is used to illustrate the application of the proposed methods.     

Tests for Structural Changes in Time Series of Counts

We propose methods for detecting structural changes in time series with discrete‐valued observations. The detector statistics come in familiar L2‐type formulations incorporating the empirical probability generating function. Special emphasis is given to the popular models of integer autoregression and Poisson autoregression. For both models, we study mainly structural changes due to a change in distribution, but we also comment for the classical problem of parameter change. The asymptotic properties of the proposed test statistics are studied under the null hypothesis as well as under alternatives. A Monte Carlo power study on bootstrap versions of the new methods is also included along with a real data example.     

Robust modal estimation and variable selection for single-index varying-coefficient models

In this article, we present a new efficient iteration estimation approach based on local modal regression for single-index varying-coefficient models. The resulted estimators are shown to be robust with regardless of outliers and error distributions. The asymptotic properties of the estimators are established under some regularity conditions and a practical modified EM algorithm is proposed for the new method. Moreover, to achieve sparse estimator when there exists irrelevant variables in the index parameters, a variable selection procedure based on SCAD penalty is developed to select significant parametric covariates and the well-known oracle properties are also derived. Finally, some numerical examples with various distributed errors and a real data analysis are conducted to illustrate the validity and feasibility of our proposed method.     

Variable selection in partially linear additive models for modal regression

Based on B-spline basis functions and smoothly clipped absolute deviation (SCAD) penalty, we present a new estimation and variable selection procedure based on modal regression for partially linear additive models. The outstanding merit of the new method is that it is robust against outliers or heavy-tail error distributions and performs no worse than the least-square-based estimation for normal error case. The main difference is that the standard quadratic loss is replaced by a kernel function depending on a bandwidth that can be automatically selected based on the observed data. With appropriate selection of the regularization parameters, the new method possesses the consistency in variable selection and oracle property in estimation. Finally, both simulation study and real data analysis are performed to examine the performance of our approach.     

Fourier–type tests involving martingale difference processes

We develop testing procedures which detect if the observed time series is a martingale difference sequence. Furthermore, tests are developed that detect change–points in the conditional expectation of the series given its past. The test statistics are formulated following the approach of Fourier–type conditional expectations first proposed by Bierens (1982 Bierens, H. J. (1982). Consistent model speci?cation tests. J. Econometr. 20:105–134.[Crossref], [Web of Science ®] , [Google Scholar]) and have the advantage of computational simplicity. The limit behavior of the test statistics is investigated under the null hypothesis as well as under alternatives. Since the asymptotic null distribution contains unknown parameters, a bootstrap procedure is proposed in order to actually perform the test. The performance of the bootstrap version of the test is compared in finite samples with other methods for the same problem. A real–data application is also included.     

创新网络中知识转移“度”及其维度

在创新网络中,知识转移及其转化程度直接影响创新网络成员之间的关系及创新结果。网络中知识转移的一个突出问题是知识转移存在很多限制性因素,而一旦限制性因素被突破,则知识转移又表现为其他特征,例如知识转移速度加快、知识转移范围扩大等。创新网络中知识转移"度",指网络成员在一定的制度约束下或者在内外条件约束下,知识转移活动所能达到的程度。在知识转移过程中,接受组织的吸收能力、知识的显性度、各种智力资本的复杂性以及知识转移背景的相似性等方面左右了知识转移的广度、速度和深度。作为知识产品的科技成果,其可转移性的高低是影响转化的根本因素。为加快中国高校科技成果的转移,必须改进现有的高校科研导向、科技成果评价机制,提高高校科研人员开展面向产业化研究的积极性,加强企业创新能力和科技成果转移体系建设。     

国家文化治理:发展文化产业的新维度

寻求人类社会发展的新的文明发展方式和生活方式,转变人类财富的增长方式,是人类社会共同追求的目标。文化产业被认为是最能体现这一价值追求的实现方式。于是,文化产业在社会发展的层面上和全球化治理的层面上便超越了法兰克福学派作为社会批判理论的"文化工业论",转而成为用以克服和解决经济和社会发展问题的治理工具和治理手段。中国在经历了政治治理("以阶级斗争为纲")、经济治理("以经济建设为中心")之后,正在走向文化治理("建设社会主义文化强国")。通过发展文化产业,克服与解决国家发展困境,建构国家文化治理,成为中国发展文化产业和实现建设文化强国的战略出发点和新维度。国家文化治理的核心在于:发展文化产业的目的不是为了经济,而是为了完善国家治理,是以经济——市场经济的方式实现文化的政治、经济、社会和文化的价值性转换,进而改变和重塑国家治理模式。     

中国住房保障政策实施效果评估——基于1997—2010年的数据

住房保障政策是当前中国最重要的经济和社会政策之一,但社会各界对住房保障政策实施效果缺乏统一认识,存在较大争议。本文基于1997—2010年国家宏观数据,在控制国民财富和城市化等因素的基础上,评估了住房保障政策的实施效果,重点分析了长期住房保障政策和2008年短期政府强力干预政策冲击的影响。结果发现,长期住房保障政策显著平抑了房价,但并没有抑制住房购买量,增强了民众购买价廉物美住房的可能,总体上增进了国民福利;2008年政府强力干预政策冲击与长期住房保障政策联手显著抑制了房价上涨,对住房销售量也有负向作用。在研究结论基础上,本文解释了研究结论与现实存在差异的原因,并提出了协调长短期住房保障政策、保障住房政策实施效果等建议。