非参数ACD模型及其在中国股票市场的实证研究

文章利用中国证券市场的日内交易数据实证了非参数ACD模型。非参数ACD模型不依赖条件均值的函数形式和误差项的分布形式,更具有一般意义。文章从多个方面进行实证分析。利用非参数方法进行分析的结果表明:数据不能用线性ACD模型来刻画,根据非参数拟合曲面的形状可以把此ACD模型的函数形式设定为某种非线性形式。     

A semiparametric approach for modelling multivariate nonlinear time series

In this article, a semiparametric time‐varying nonlinear vector autoregressive (NVAR) model is proposed to model nonlinear vector time series data. We consider a combination of parametric and nonparametric estimation approaches to estimate the NVAR function for both independent and dependent errors. We use the multivariate Taylor series expansion of the link function up to the second order which has a parametric framework as a representation of the nonlinear vector regression function. After the unknown parameters are estimated by the maximum likelihood estimation procedure, the obtained NVAR function is adjusted by a nonparametric diagonal matrix, where the proposed adjusted matrix is estimated by the nonparametric kernel estimator. The asymptotic consistency properties of the proposed estimators are established. Simulation studies are conducted to evaluate the performance of the proposed semiparametric method. A real data example on short‐run interest rates and long‐run interest rates of United States Treasury securities is analyzed to demonstrate the application of the proposed approach. The Canadian Journal of Statistics 47: 668–687; 2019 © 2019 Statistical Society of Canada     

市场力量视角下中国小麦进口市场结构研究

基于2005—2014年的相关月度数据,通过构建RDE模型,分析了中国小麦进口市场上主要进口来源国的市场力量。结果表明:美国的市场力量最强,加拿大次之,澳大利亚的市场力量最弱;中国将"适度进口"纳入国家粮食安全战略的政策实施后,能够在一定程度上提升澳大利亚的市场力量。但是,上述三个国家仍然不能实现基于市场力量的相互制衡,导致中国小麦进口市场结构缺乏稳定性。     

非参数可加ACD模型及其应用研究

非参数可加ACD模型对条件期望的函数形式与随机误差项的分布形式要求都没有参数ACD模型强，因此不会像参数ACD模型那样因模型形式设定错误而得出错误结论。非参数可加ACD模型估计出来的各个可加部分图形的形状对于正确设定参数ACD模型具有一定的指导作用。     

中美贸易摩擦、美国进口冲击与中国企业创新

在中美贸易摩擦不断升级的背景下,基于高度细化的微观企业数据,本文实证分析了来自美国的进口竞争对中国企业创新的影响。研究发现：①从整体上来看,来自美国的进口竞争优化了企业的专利申请结构,对企业发明专利申请量具有显著促进作用,对企业实用新型专利和外观设计专利申请量无显著影响。②来自美国的进口竞争,对内资企业、低融资约束企业的创新总产出具有显著促进作用,创新总产出增加是由于高质量创新（发明专利）与低质量创新（实用新型专利、外观设计专利）同时显著提升导致的;外资企业、高融资约束企业的创新总产出下降是由于低质量创新显著下降、高质量创新变化不显著导致的,对低生产率企业高质量创新的促进作用大于高生产率企业。③来自美国的进口竞争增加了企业退出市场的风险,为了应对美国进口冲击,中国企业积极应对,增加研发投入支出、优化就业结构、扩大就业规模,加快高质量创新,从而产生了“逃离竞争效应”。     

Smoothing parameter selection methods for nonparametric regression with spatially correlated errors

When spatial data are correlated, currently available data‐driven smoothing parameter selection methods for nonparametric regression will often fail to provide useful results. The authors propose a method that adjusts the generalized cross‐validation criterion for the effect of spatial correlation in the case of bivariate local polynomial regression. Their approach uses a pilot fit to the data and the estimation of a parametric covariance model. The method is easy to implement and leads to improved smoothing parameter selection, even when the covariance model is misspecified. The methodology is illustrated using water chemistry data collected in a survey of lakes in the Northeastern United States.     

Variable selection in proportional hazards cure model with time-varying covariates,application to US bank failures*

From a survival analysis perspective, bank failure data are often characterized by small default rates and heavy censoring. This empirical evidence can be explained by the existence of a subpopulation of banks likely immune from bankruptcy. In this regard, we use a mixture cure model to separate the factors with an influence on the susceptibility to default from the ones affecting the survival time of susceptible banks. In this paper, we extend a semi-parametric proportional hazards cure model to time-varying covariates and we propose a variable selection technique based on its penalized likelihood. By means of a simulation study, we show how this technique performs reasonably well. Finally, we illustrate an application to commercial bank failures in the United States over the period 2006–2016.     

Nonparametric Nonlinear Cotrending Analysis,With an Application to Interest and Inflation in the United States

Given the assumption that the components of a vector time series are stationary around nonlinear deterministic time trends, nonlinear cotrending is the phenomenon that one or more linear combinations of the time series are stationary around a linear trend or a constant; hence, the series have common nonlinear deterministic time trends. In this article, I develop nonparametric tests for nonlinear cotrending, and I derive nonparametric estimators of the cotrending vectors. I apply this approach to the federal funds rate and the consumer price index inflation rate in the United States, using monthly data, to analyze the price puzzle.     

Nonparametric modeling of clustered customer survival data

We incorporate a random clustering effect into the nonparametric version of Cox Proportional Hazards model to characterize clustered survival data. The simulation studies provide evidence that clustered survival data can be better characterized through a nonparametric model. Predictive accuracy of the nonparametric model is affected by number of clusters and distribution of the random component accounting for clustering effect. As the functional form of the covariate departs from linearity, the nonparametric model is becoming more advantageous over the parametric counterpart. Finally, nonparametric is better than parametric model when data are highly heterogenous and/or there is misspecification error.     

全球通货膨胀的驱动因素与非对称冲击分析——基于扩展的新凯恩斯菲利普斯曲线的实证分析

将全球流动性、全球工资水平、美元汇率、国际资源品价格、全球经济增长等因素引入具有微观基础的新凯恩斯菲利普斯曲线模型中,利用状态空间模型实证分析了各变量对全球通货膨胀影响的变动状况,分析了美国和中国CPI的驱动因素。结果表明：全球流动性是全球通货膨胀产生的最主要驱动因素,其次是美元汇率、全球经济增长和国际资源品价格,三者具有持续且稳定的影响力。中国需求因素影响逐渐增强。全球工资水平对通货膨胀的影响不明显。美国CPI的重要推动因素是居民可支配收入,中国CPI的重要推动因素是经济增长率和国际资源品价格。     

Nonparametric Testing for Asymmetric Information

Asymmetric information is an important phenomenon in many markets and in particular in insurance markets. Testing for asymmetric information has become a very important issue in the literature in the last two decades. Almost all testing procedures that are used in empirical studies are parametric, which may yield misleading conclusions in the case of misspecification of either functional or distributional relationships among the variables of interest. Motivated by the literature on testing conditional independence, we propose a new nonparametric test for asymmetric information, which is applicable in a variety of situations. We demonstrate that the test works reasonably well through Monte Carlo simulations and apply it to an automobile insurance dataset and a long-term care insurance (LTCI) dataset. Our empirical results consolidate Chiappori and Salanié’s findings that there is no evidence for the presence of asymmetric information in the French automobile insurance market. While Finkelstein and McGarry found no positive correlation between risk and coverage in the LTCI market in the United States, our test detects asymmetric information using only the information that is available to the insurance company, and our investigation of the source of asymmetric information suggests some sort of asymmetric information that is related to risk preferences as opposed to risk types and thus lends support to Finkelstein and McGarry.     

An empirical likelihood goodness-of-fit test for time series

Summary. Standard goodness-of-fit tests for a parametric regression model against a series of nonparametric alternatives are based on residuals arising from a fitted model. When a parametric regression model is compared with a nonparametric model, goodness-of-fit testing can be naturally approached by evaluating the likelihood of the parametric model within a nonparametric framework. We employ the empirical likelihood for an α -mixing process to formulate a test statistic that measures the goodness of fit of a parametric regression model. The technique is based on a comparison with kernel smoothing estimators. The empirical likelihood formulation of the test has two attractive features. One is its automatic consideration of the variation that is associated with the nonparametric fit due to empirical likelihood's ability to Studentize internally. The other is that the asymptotic distribution of the test statistic is free of unknown parameters, avoiding plug-in estimation. We apply the test to a discretized diffusion model which has recently been considered in financial market analysis.     

Bayesian nonparametric modelling of the link function in the single-index model using a Bernstein–Dirichlet process prior

This paper proposes the use of the Bernstein–Dirichlet process prior for a new nonparametric approach to estimating the link function in the single-index model (SIM). The Bernstein–Dirichlet process prior has so far mainly been used for nonparametric density estimation. Here we modify this approach to allow for an approximation of the unknown link function. Instead of the usual Gaussian distribution, the error term is assumed to be asymmetric Laplace distributed which increases the flexibility and robustness of the SIM. To automatically identify truly active predictors, spike-and-slab priors are used for Bayesian variable selection. Posterior computations are performed via a Metropolis-Hastings-within-Gibbs sampler using a truncation-based algorithm for stick-breaking priors. We compare the efficiency of the proposed approach with well-established techniques in an extensive simulation study and illustrate its practical performance by an application to nonparametric modelling of the power consumption in a sewage treatment plant.     

Doubly periodic non-homogeneous Poisson models for hurricane data

Non-homogeneous Poisson processes with periodic claim intensity rate have been proposed as claim counts in risk theory. Here a doubly periodic Poisson model with short- and long-term trends is studied. Beta-type intensity functions are presented as illustrations. The likelihood function and the maximum likelihood estimates of the model parameters are derived.Doubly periodic Poisson models are appropriate when the seasonality does not repeat exactly the same short-term pattern every year, but has a peak intensity that varies over a longer period. This reflects periodic environments like those forming hurricanes, in alternating El Niño/La Niña years. An application of the model to the data set of Atlantic hurricanes affecting the United States (1899–2000) is discussed in detail.     

A monte carlo study of the friedman and conover tests in the single-factor repeated measures design

A Monte Carlo study was used to compare the Type I error rates and power of two nonparametric tests against the F test for the single-factor repeated measures model. The performance of the nonparametric Friedman and Conover tests was investigated for different distributions, numbers of blocks and numbers of repeated measures. The results indicated that the type of the distribution has little effect on the ability of the Friedman and Conover tests to control Type error rates. For power, the Friedman and Conover tests tended to agree in rejecting the same false hyporhesis when the design consisted of three repeated measures. However, the Conover test was more powerful than the Friedman test when the number of repeated measures was 4 or 5. Still, the F test is recommended for the single-factor repeated measures model because of its robustness to non-normality and its good power across a range of conditions.     

用函数型数据分析网络团购市场的结构与市场发展

以美团网为例,使用函数型数据研究网络团购市场的结构与发展。首先将从网络采集到的团购离散数据,根据分析目的构造成函数化数据,然后对函数化的数据进行描述统计分析和函数主成分分析,并对不同种类的团购与不同地区的团购进行比较。最终得出区别于以往研究团购的结论:当期团购市场仍以美食类为主,购物类团购逐渐被大家认同,休闲类团购集中度下降;上海与北京都属于团购发展较快地区,而北京地区相对变动较大;热门地区团购销售额的集中度随着团购地区的扩张而被稀释。     

流动性信息与资产收益：基于非参数模型的分析

本文首先从全新的角度给出市场深度指标的求解方法,然后结合条件资产定价模型和自回归条件异方差模型的优势建立半变系数模型,并应用于我国股票市场每日收益率的研究,得出四点判断：1、通过非参数方法求解得到的指标值具有显著的平稳性。2、流动性信息的时变性对股票市场存在显著的非线性冲击,而且流动性信息的持续性变化与收益之间存在负向关系。3、得到和经典资产定价模型相同的结论,即市场综合指数对个股具有显著的影响。4、模型验证了流动性信息通过波动性将信息非线性传导给投资者的假设,伴随着流动性信息的时变性,投资者所得到的风险补偿也具备时变性。5、通过实际数据的验证,我们所建立的半变系数模型能够较好的解释流动性信息的传递,也为我们以后的实证研究提供了一个估计和检验流动性信息传导和时变型风险补偿的新方法。     

A finite sample comparison of nonparametric estimates of the effective dose in quantal bioassay

To estimate the effective dose level ED_α in the common binary response model, several parametric and nonparametric estimators have been proposed in the literature. In the present article, we focus on nonparametric methods and present a detailed numerical comparison of four different approaches to estimate the ED_α nonparametrically. The methods are briefly reviewed and their finite sample properties are studied by means of a detailed simulation study. Moreover, a data example is presented to illustrate the different concepts.     

Estimation and testing in generalized partial linear models—A comparative study

A particular semiparametric model of interest is the generalized partial linear model (GPLM) which extends the generalized linear model (GLM) by a nonparametric component.The paper reviews different estimation procedures based on kernel methods as well as test procedures on the correct specification of this model (vs. a parametric generalized linear model). Simulations and an application to a data set on East–West German migration illustrate similarities and dissimilarities of the estimators and test statistics.     

Analytic computation of nonparametric Marsan–Lengliné estimates for Hawkes point processes

In 2008, Marsan and Lengliné presented a nonparametric way to estimate the triggering function of a Hawkes process. Their method requires an iterative and computationally intensive procedure which ultimately produces only approximate maximum likelihood estimates (MLEs) whose asymptotic properties are poorly understood. Here, we note a mathematical curiosity that allows one to compute, directly and extremely rapidly, exact MLEs of the nonparametric triggering function. The method here requires that the number q of intervals on which the nonparametric estimate is sought equals the number n of observed points. The resulting estimates have very high variance but may be smoothed to form more stable estimates. The performance and computational efficiency of the proposed method is verified in two disparate, highly challenging simulation scenarios: first to estimate the triggering functions, with simulation-based 95% confidence bands, for earthquakes and their aftershocks in Loma Prieta, California, and second, to characterise triggering in confirmed cases of plague in the United States over the last century. In both cases, the proposed estimator can be used to describe the rate of contagion of the processes in detail, and the computational efficiency of the estimator facilitates the construction of simulation-based confidence intervals.