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。     

一种改进的非参数方法对金融价值风险的估计

文章对利用波动率计算价值风险VaR的方法进行了改进,提出了非参数波动率结合非参数条件核密度条件分位数方法来计算VaR,此非参数方法克服了模型误设的问题,不受波动率模型具体形式的限制,不受新息项分布函数的限制,是一种稳健的适应性方法.同时将此方法应用到中小板综指与创业版指进行实证分析,与相应的半参数及参数方法进行比较,发现文中提出的方法在某种程度上比较稳定可靠.     

基于金融高频数据的ACD模型非参数设定检验

 目前关于ACD的实证研究已经十分丰富，却很少有人把注意力放在ACD及其扩展模型设定的检验上，本文采用的D检验就是通过衡量残差密度函数的参数和非参数估计值之间的紧密程度，来检验模型设定的优劣。     

一种非参数可加模型回归估计的简便方法

文章利用非参数可加模型来构造经济增长函数.从研究方法看,文章采用了一种较为简便的非参回归估计方法构造矩阵,并运用Backfitting算法来对该函数进行迭代求解.该方法简单明了,在程序实现过程中避免了选择窗宽的繁冗过程;从实证的角度看,文章从两个方面来考虑经济增长的影响因素:外商直接投资和普通高校招生情况,这两方面皆被目前人们所普遍关注.实证结果揭示了影响经济增长的一些重要特征.     

基于非参数GARCH模型的汇率波动性预测

文章利用非参数GARCH模型来预测人民币汇率的波动性,并且与参数GARCH族模型的预测结果进行比较。理论上,非参数GARCH模型避免了参数GARCH族模型形式上的错误设定,具有稳健性。文章选择美元和日元兑人民币汇率的日对数收益率来进行预测,预测结果综合表明非参数GARCH模型具有最强的预测能力。     

多水平C-D生产函数模型及其参数异质性研究

文章利用多水平模型分析方法建立了多水平C-D生产函数模型。该模型考虑了层次结构及异质性对C-D生产函数的影响,并基于我国1997~2007年经济发展相关数据进行了实证建模和数据分析。结果表明,多水平模型能够更好地反映我国经济发展规律,同时发现其资本贡献率显著依赖地区对外开放度,从而揭示了生产函数模型的参数异质性特征。文章还通过拟合模型,估算了各省份资本要素的贡献份额及其与对外开放度的关系。     

基于样条估计的非参数修正权证定价研究

文章针对权证市场的特点,提出适用于权证市场的基于样条估计的依模型非参数修正权证定价方法。该方法首次将样条估计应用于生存函数积分形式的估计中,它综合了参数定价模型和非参数定价方法的优点,既包含了实际市场的先验信息,又不乏非参数定价模型的灵活性。实证分析结果表明该定价方法在权证定价和价格预报方面明显好于市场上常用的定价方法,为我国个股期权的正式推出以及期权市场的健康发展提供一定的基础。     

中国A股市场的风险厌恶度量

提出了非参数ARCH-M模型,给出了模型的局部估计方法。对2000-2005年间中国A股日市场综合收益率数据进行实证分析,进一步探讨了风险厌恶的度量问题。研究结果表明与常数风险厌恶模型相比,非参数化后的ARCH-M模型能较好地捕捉了变化趋势,且模型的预测精度也得到了提高。     

Logistic半参数变系数模型的变量选择

Logistic半参数变系数模型是半参数变系数模型的推广,它可以解决分类型因变量变系数模型的建模问题.文章利用B样条函数逼近非参数部分,引入LASSO、SCAD以及MCP惩罚函数,基于组坐标下降算法,对参数部分和非参数部分进行变量选择.最后进行了Monte Carlo模拟.     

非参数异方差模型中条件回归函数的EM算法——基于农村食品消费与纯收入的实证研究

对非参数异方差模型中回归函数的EM算法进行研究,并基于EM算法得到了条件回归函数的估计。此外,通过对农村居民食品消费支出与纯收入关系的实证分析,说明了基于EM算法的估计方法比最小二乘估计方法的拟合效果更好,并对恩格尔系数进行了拟合,分析了其变化走势。     

Robust signed-rank estimation and variable selection for semi-parametric additive partial linear models

A fully nonparametric model may not perform well or when the researcher wants to use a parametric model but the functional form with respect to a subset of the regressors or the density of the errors is not known. This becomes even more challenging when the data contain gross outliers or unusual observations. However, in practice the true covariates are not known in advance, nor is the smoothness of the functional form. A robust model selection approach through which we can choose the relevant covariates components and estimate the smoothing function may represent an appealing tool to the solution. A weighted signed-rank estimation and variable selection under the adaptive lasso for semi-parametric partial additive models is considered in this paper. B-spline is used to estimate the unknown additive nonparametric function. It is shown that despite using B-spline to estimate the unknown additive nonparametric function, the proposed estimator has an oracle property. The robustness of the weighted signed-rank approach for data with heavy-tail, contaminated errors, and data containing high-leverage points are validated via finite sample simulations. A practical application to an economic study is provided using an updated Canadian household gasoline consumption data.     

Short-term forecasting with a computationally efficient nonparametric transfer function model

In this paper a semi-parametric approach is developed to model non-linear relationships in time series data using polynomial splines. Polynomial splines require very little assumption about the functional form of the underlying relationship, so they are very flexible and can be used to model highly non-linear relationships. Polynomial splines are also computationally very efficient. The serial correlation in the data is accounted for by modelling the noise as an autoregressive integrated moving average (ARIMA) process, by doing so, the efficiency in nonparametric estimation is improved and correct inferences can be obtained. The explicit structure of the ARIMA model allows the correlation information to be used to improve forecasting performance. An algorithm is developed to automatically select and estimate the polynomial spline model and the ARIMA model through backfitting. This method is applied on a real-life data set to forecast hourly electricity usage. The non-linear effect of temperature on hourly electricity usage is allowed to be different at different hours of the day and days of the week. The forecasting performance of the developed method is evaluated in post-sample forecasting and compared with several well-accepted models. The results show the performance of the proposed model is comparable with a long short-term memory deep learning model.     

A generalized likelihood ratio test for monitoring profile data

Profile data emerges when the quality of a product or process is characterized by a functional relationship among (input and output) variables. In this paper, we focus on the case where each profile has one response variable Y, one explanatory variable x, and the functional relationship between these two variables can be rather arbitrary. The basic concept can be applied to a much wider case, however. We propose a general method based on the Generalized Likelihood Ratio Test (GLRT) for monitoring of profile data. The proposed method uses nonparametric regression to estimate the on-line profiles and thus does not require any functional form for the profiles. Both Shewhart-type and EWMA-type control charts are considered. The average run length (ARL) performance of the proposed method is studied. It is shown that the proposed GLRT-based control chart can efficiently detect both location and dispersion shifts of the on-line profiles from the baseline profile. An upper control limit (UCL) corresponding to a desired in-control ARL value is constructed.     

Functional convergence of quantile-type frontiers with application to parametric approximations

Nonparametric estimators of the upper boundary of the support of a multivariate distribution are very appealing because they rely on very few assumptions. But in productivity and efficiency analysis, this upper boundary is a production (or a cost) frontier and a parametric form for it allows for a richer economic interpretation of the production process under analysis. On the other hand, most of the parametric approaches rely on often too restrictive assumptions on the stochastic part of the model and are based on standard regression techniques fitting the shape of the center of the cloud of points rather than its boundary. To overcome these limitations, Florens and Simar [2005. Parametric approximations of nonparametric frontiers. J. Econometrics 124 (1), 91–116] propose a two-stage approach which tries to capture the shape of the cloud of points near its frontier by providing parametric approximations of a nonparametric frontier. In this paper we propose an alternative method using the nonparametric quantile-type frontiers introduced in Aragon, Daouia and Thomas-Agnan [2005. Nonparametric frontier estimation: a conditional quantile-based approach. Econometric Theory 21, 358–389] for the nonparametric part of our model. These quantile-type frontiers have the superiority of being more robust to extremes. Our main result concerns the functional convergence of the quantile-type frontier process. Then we provide convergence and asymptotic normality of the resulting estimators of the parametric approximation. The approach is illustrated through simulated and real data sets.     

A diagnostic statistic for functional-coefficient autoregressive models

In this paper, functional coefficient autoregressive (FAR) models proposed by Chen and Tsay (1993) are considered. We propose a diagnostic statistic for FAR models constructed by comparing between parametric and nonparametric estimators of the functional form of the FAR models. We show asymptotic properties of our statistic mathematically and it can be applied to the estimation of the delay parameter and the specification of the functional form of FAR models.     

Regression models for binary longitudinal responses

Some conditional models to deal with binary longitudinal responses are proposed, extending random effects models to include serial dependence of Markovian form, and hence allowing for quite general association structures between repeated observations recorded on the same individual. The presence of both these components implies a form of dependence between them, and so a complicated expression for the resulting likelihood. To handle this problem, we introduce, as a first instance, what Follmann and Wu (1995) called, in a different setting, an approximate conditional model, which represents an optimal choice for the general framework of categorical longitudinal responses. Then we define two more formally correct models for the binary case, with no assumption about the distribution of the random effect. All of the discussed models are estimated by means of an EM algorithm for nonparametric maximum likelihood. The algorithm, an adaptation of that used by Aitkin (1996) for the analysis of overdispersed generalized linear models, is initially derived as a form of Gaussian quadrature, and then extended to a completely unknown mixing distribution. A large scale simulation work is described to explore the behaviour of the proposed approaches in a number of different situations.     

A general maximum likelihood analysis of measurement error in generalized linear models

This paper describes an EM algorithm for maximum likelihood estimation in generalized linear models (GLMs) with continuous measurement error in the explanatory variables. The algorithm is an adaptation of that for nonparametric maximum likelihood (NPML) estimation in overdispersed GLMs described in Aitkin (Statistics and Computing 6: 251–262, 1996). The measurement error distribution can be of any specified form, though the implementation described assumes normal measurement error. Neither the reliability nor the distribution of the true score of the variables with measurement error has to be known, nor are instrumental variables or replication required.Standard errors can be obtained by omitting individual variables from the model, as in Aitkin (1996).Several examples are given, of normal and Bernoulli response variables.     

Estimation for generalized partially functional linear additive regression model

In practice, it is not uncommon to encounter the situation that a discrete response is related to both a functional random variable and multiple real-value random variables whose impact on the response is nonlinear. In this paper, we consider the generalized partial functional linear additive models (GPFLAM) and present the estimation procedure. In GPFLAM, the nonparametric functions are approximated by polynomial splines and the infinite slope function is estimated based on the principal component basis function approximations. We obtain the estimator by maximizing the quasi-likelihood function. We investigate the finite sample properties of the estimation procedure via Monte Carlo simulation studies and illustrate our proposed model by a real data analysis.