具有AR(p)误差的半参数回归模型的参数估计

近年来,半参数模型是处理回归问题的有力工具,进年来,已经成为当今回归分析的热点,引起了众多学者的关注。文章研究了具有AR(p)误差的半参数回归模型,首先对其误差的相关性进行了消除,然后将模型转变成为经典的半参数回归模型,运用惩罚最小二乘估计方法对模型参数进行了估计。     

半参数趋势阈值面板模型及其参数估计

半参数趋势面板数据模型在社会经济问题的实证分析中具有很强的适用性,但现有的研究中,半参数趋势面板模型考虑了时间趋势的非线性,但没有考虑政策等因素对参数的影响。本文将结构突变理论引入截面相关下的半参数趋势面板模型,并基于PPLE方法,建立了有效估计量和识别程序。通过仿真实验和实证应用,验证了对于含有突变点的半参数趋势面板模型,EPPLE方法的参数估计是有效的。     

生产率增长测算的半参数估计方法:理论综述和相关探讨

半参数模型是参数和非参数回归模型的一种概括统一，其中的参数分量部分用于对确定性影响因素进行分析，而非参数分量部分则用于对随机干扰因素的刻画。Olley和Pakes最早给出了关于生产率增长测算的半参数估计方法的研究，Ackerberg和Caves对这一研究进行了修正和补充。但由于理论发展方面还不够成熟，限制了方法的实证应用。半参数方法对经济现实的描述更接近真实，随着半参数估计理论的日渐成熟，半参数估计方法在生产率增长测算领域必将发挥越来越大的作用。     

半参数变系数空间误差回归模型的估计

研究目标：克服半参数变系数回归模型中误差项可能存在的空间相关性问题。研究方法：提出一类新的半参数变系数空间误差回归模型,并构造其截面似然估计。研究发现：在小样本条件下,模型估计量具有良好的表现,其精度随着样本容量的增加而提高;应用该方法分析我国资源禀赋与地方公共品供给之间的相互关系,进一步证实了模型较强的适用性。研究创新：证明了估计量的一致性与渐近正态性,并通过蒙特卡洛模拟考察了估计方法的小样本表现。研究价值：新方法对于其他结构的半/非参数空间计量模型理论研究具有推广价值,其估计技术在经济、管理等学科中具有应用价值。     

时空动态半参数变系数随机效应面板模型的估计

研究目标：克服半参数变系数面板模型中扰动项和因变量存在时空动态性问题。研究方法：提出一类更加一般化的时空动态半参数变系数随机效应面板模型,并构建截面似然估计量。研究发现：估计量具有良好的小样本性质,估计误差随着样本总量的提高而减小,在Case空间矩阵下,空间滞后和时空滞后系数的估计精度随空间复杂度的增大而降低,用该方法分析我国外商直接投资、知识产权保护与经济增长关系,进一步证实了模型的适用性。研究创新：证明了估计量满足一致性和渐近正态性,数值模拟考察了估计量的小样本性质。研究价值：拓展了现有半参数变系数空间面板模型的形式,增强了模型的适用性和解释力,有益于经济问题实证研究的开展。     

半参数变系数空间滞后模型的截面极大似然估计

在线性参数空间滞后模型中,解释变量的系数一般假设为固定常数,本文首先放松了这种假设,将解释变量的系数设定为某一变量的未知函数,提出一类全新的半参数变系数空间滞后模型;其次导出了该模型的截面极大似然估计,并证明了该估计的一致性;最后用蒙特卡洛数值模拟方法考察了该估计在小样本条件下的性质,数值模拟结果显示我们提出的估计方法在小样本条件下依然有优良的表现。     

商品房价格指数的半参数分析

本文利用半参数分析的原理与方法，结合商品房销售数据的特点，建立商品房价格指数的半参数回归模型。并进一步讨论半参数模型的估计方法、Hausman假设检验及其参数标准误差的自展法计算。通过实例分析，表明对商品房价格指数的处理，半参数回归分析的效果优于普通的最小二乘法。     

GARCH族模型的预测能力比较:一种半参数方法

半参数GARCH模型无须设定条件分布的具体形式。本文首先将一种效率较高、易于实施的半参数方法——估计函数方法应用于10类常见的GARCH结构,并给出证据,显示该方法能显著提高GARCH族模型的波动率预测绩效。然后,应用估计函数方法,较为全面地比较各类GARCH结构的预测能力。为给出统计意义下的结果,并减少数据窥察问题,研究中分别使用OLS和SPA检验法进行绩效评价。结果发现,与其他GARCH类结构相比,EGARCH和APARCH模型能够较好地描述股市收益率的波动过程。     

焦炭价格的半参数估计

文章以1997年1月到2009年4月焦炭消费市场数据为例,构建了焦炭价格估计的半参数模型。一次线性估计结果显示半参数回归模型的参数部分取焦炭产量,非参数部分取生铁价格。     

深证成分指数的股市风险分析——基于EGARCH-M-VaR模型的半参数法应用

提出了基于EGARCH-M-VaR的半参数方法,根据EGARCH-M模型的参数估计得到条件标准差,并进一步算出VaR,算出溢出率。同时计算出基于正态分布和学生-t分布假设下的GARCH模型、EGARCH-M模型对应的VaR值及溢出率。通过统计分析和后验测试等实证研究表明,基于EGARCH(1,1)-M-VaR的半参数方法在刻画深圳股票市场风险方面有明显的优势。     

Properties of Census Dual System Population Size Estimators

We study parametric and non‐parametric approaches for assessing the accuracy and coverage of a population census based on dual system surveys. The two parametric approaches being considered are post‐stratification and logistic regression, which have been or will be implemented for the US Census dual system surveys. We show that the parametric model‐based approaches are generally biased unless the model is correctly specified. We then study a local post‐stratification approach based on a non‐parametric kernel estimate of the Census enumeration functions. We illustrate that the non‐parametric approach avoids the risk of model mis‐specification and is consistent under relatively weak conditions. The performances of these estimators are evaluated numerically via simulation studies and an empirical analysis based on the 2000 US Census post‐enumeration survey data.     

Statistical Decision Problems and Bayesian Nonparametric Methods

This paper considers parametric statistical decision problems conducted within a Bayesian nonparametric context. Our work was motivated by the realisation that typical parametric model selection procedures are essentially incoherent. We argue that one solution to this problem is to use a flexible enough model in the first place, a model that will not be checked no matter what data arrive. Ideally, one would use a nonparametric model to describe all the uncertainty about the density function generating the data. However, parametric models are the preferred choice for many statisticians, despite the incoherence involved in model checking, incoherence that is quite often ignored for pragmatic reasons. In this paper we show how coherent parametric inference can be carried out via decision theory and Bayesian nonparametrics. None of the ingredients discussed here are new, but our main point only becomes evident when one sees all priors—even parametric ones—as measures on sets of densities as opposed to measures on finite-dimensional parameter spaces.     

Weighted least squares estimators in possibly misspecified nonlinear regression

The behavior of estimators for misspecified parametric models has been well studied. We consider estimators for misspecified nonlinear regression models, with error and covariates possibly dependent. These models are described by specifying a parametric model for the conditional expectation of the response given the covariates. This is a parametric family of conditional constraints, which makes the model itself close to nonparametric. We study the behavior of weighted least squares estimators both when the regression function is correctly specified, and when it is misspecified and also involves possible additional covariates.     

Nonparametric likelihood ratio model selection tests between parametric likelihood and moment condition models

We propose a nonparametric likelihood ratio testing procedure for choosing between a parametric (likelihood) model and a moment condition model when both models could be misspecified. Our procedure is based on comparing the Kullback–Leibler Information Criterion (KLIC) between the parametric model and moment condition model. We construct the KLIC for the parametric model using the difference between the parametric log likelihood and a sieve nonparametric estimate of population entropy, and obtain the KLIC for the moment model using the empirical likelihood statistic. We also consider multiple (>2)$(>2)$ model comparison tests, when all the competing models could be misspecified, and some models are parametric while others are moment-based. We evaluate the performance of our tests in a Monte Carlo study, and apply the tests to an example from industrial organization.     

Bayesian semiparametric stochastic volatility modeling

This paper extends the existing fully parametric Bayesian literature on stochastic volatility to allow for more general return distributions. Instead of specifying a particular distribution for the return innovation, nonparametric Bayesian methods are used to flexibly model the skewness and kurtosis of the distribution while the dynamics of volatility continue to be modeled with a parametric structure. Our semiparametric Bayesian approach provides a full characterization of parametric and distributional uncertainty. A Markov chain Monte Carlo sampling approach to estimation is presented with theoretical and computational issues for simulation from the posterior predictive distributions. An empirical example compares the new model to standard parametric stochastic volatility models.     

Analysis of failure time using threshold regression with semi‐parametric varying coefficients

Many new statistical models may enjoy better interpretability and numerical stability than traditional models in survival data analysis. Specifically, the threshold regression (TR) technique based on the inverse Gaussian distribution is a useful alternative to the Cox proportional hazards model to analyse lifetime data. In this article we consider a semi‐parametric modelling approach for TR and contribute implementational and theoretical details for model fitting and statistical inferences. Extensive simulations are carried out to examine the finite sample performance of the parametric and non‐parametric estimates. A real example is analysed to illustrate our methods, along with a careful diagnosis of model assumptions.     

Semiparametric estimation of a hedonic price function

Previous work on the preferred specification of hedonic price models usually recommended a Box-Cox model. In this paper we note that any parametric model involves implicit restrictions and they can be reduced by using a semiparametric model. We estimate a benchmark parametric model which passes several common specification tests, before showing that a semiparametric model outperforms it significantly. In addition to estimating the model, we compare the predictions of the models by deriving the distribution of the predicted log(price) and then calculating the associated prediction intervals. Our data show that the semiparametric model provides more accurate mean predictions than the benchmark parametric model.     

Conceptual estimating tool for technology-driven projects: exploring parametric estimating technique

This paper examines a parametric estimating technique applied to technology-driven projects. Parametric cost estimating is a widely used approach for bidding on a contract, input into a cost benefit analysis, or as the pre-planning tool for project implementation. Extensive literature reviews suggest that effective parametric estimating methodology is becoming an essential tool for technology-driven organizations. The use of parametric estimating in budgeting, scheduling, and control of projects will enhance the ability of project management organizations to effectively and efficiently utilize valuable resources. The benefit of parametric estimating is its use as an estimating model for better determining potential resource requirements during the project pre-planning and conceptual phase.     

Distribution-free and link-free estimation for the sample selection model

The prevalent estimation methods for the sample selection model rely heavily on parametric assumptions and are sensitive to departures from the underlying parametric assumptions [see, e.g., Goldberger (1983)]. We propose an alternative estimation method, the corrected maximum likelihood estimate, which is consistent for the slope vector in the outcome equation up to a multiplicative scalar, even through the parametric model on which the estimate is based might be misspecified. As an important corollary, it follows from our result that Olsen's (1980) corrected ordinary least squares estimate is consistent if the outcome equation is linear, without requiring Olsen's assumptions on the joint error distribution.     

Realized Laplace transforms for estimation of jump diffusive volatility models

We develop an efficient and analytically tractable method for estimation of parametric volatility models that is robust to price-level jumps. The method entails first integrating intra-day data into the Realized Laplace Transform of volatility, which is a model-free estimate of the daily integrated empirical Laplace transform of the unobservable volatility. The estimation is then done by matching moments of the integrated joint Laplace transform with those implied by the parametric volatility model. In the empirical application, the best fitting volatility model is a non-diffusive two-factor model where low activity jumps drive its persistent component and more active jumps drive the transient one.