The LASSO Method for Bilinear Time Series Models

In this article we propose a method called GLLS for the fitting of bilinear time series models. The GLLS procedure is the combination of the LASSO method, the generalized cross-validation method, the least angle regression method, and the stepwise regression method. Compared with the traditional methods such as the repeated residual method and the genetic algorithm, GLLS has the advantage of shrinking the coefficients of the models and saving the computational time. The Monte Carlo simulation studies and a real data example are reported to assess the performance of the proposed GLLS method.     

多重视角下的最大化双边特征法及其加权形式

在国际经济比较中,可加性和特征性是购买力平价汇总方法应该具备的重要性质。针对新近发展的、基于双边指数所构建的兼具可加性与特征性的多边比较方法———MBC法,本文对其进行了拓展研究,发展出更为完备的MBC法:一方面,鉴于原始极值最优视角下的MBC法主要考虑的是存在较多“奇异”型价格结构的国家双边比较的特征性,为弥补该视角下的MBC法难以实现整体最优的不足,本文从最小二乘和对数最小二乘的视角分别构建MBC法;另一方面,进一步在MBC法中考虑不同双边比较的可靠性差异问题,分别构建多重视角下的加权MBC法,并引入多种权重函数供应用者参考。采用 ICP2011数据的实证研究结果充分说明,多重视角下的MBC法较好地整合了官方GEKS法的特征性优点和 GK法的可加性优点,相比已有满足可加性的方法,MBC法替代偏差最小,是较理想的既适合于实际物量比较、又可用作支出结构分析的方法。实证研究还发现,最小二乘和对数最小二乘视角下的加权MBC法稳健性较好的优点。     

A new information criterion-based bandwidth selection method for non-parametric regressions

ABSTRACT

Local linear estimator is a popularly used method to estimate the non-parametric regression functions, and many methods have been derived to estimate the smoothing parameter, or the bandwidth in this case. In this article, we propose an information criterion-based bandwidth selection method, with the degrees of freedom originally derived for non-parametric inferences. Unlike the plug-in method, the new method does not require preliminary parameters to be chosen in advance, and is computationally efficient compared to the cross-validation (CV) method. Numerical study shows that the new method performs better or comparable to existing plug-in method or CV method in terms of the estimation of the mean functions, and has lower variability than CV selectors. Real data applications are also provided to illustrate the effectiveness of the new method.     

应用Bootstrap模拟技术的VaR计算与分析研究

比较估计VaR的方差一协方差方法、历史模拟法和MonteCarlo模拟方法，提出估计VaR的新模型和新方法。新模型把随机收益率分解成刻画收益率二阶矩特征的部分和刻画随机均值的正态部分。新方法使用Bootstrap模拟技术和MonteCarlo模拟技术计算VaR。实证分析表明，新方法计算的VaR在传统方法计算的VaR参考范围内。     

Bias Reduction in Logistic Regression with Missing Responses When the Missing Data Mechanism is Nonignorable

ABSTRACT

In logistic regression with nonignorable missing responses, Ibrahim and Lipsitz proposed a method for estimating regression parameters. It is known that the regression estimates obtained by using this method are biased when the sample size is small. Also, another complexity arises when the iterative estimation process encounters separation in estimating regression coefficients. In this article, we propose a method to improve the estimation of regression coefficients. In our likelihood-based method, we penalize the likelihood by multiplying it by a noninformative Jeffreys prior as a penalty term. The proposed method reduces bias and is able to handle the issue of separation. Simulation results show substantial bias reduction for the proposed method as compared to the existing method. Analyses using real world data also support the simulation findings. An R package called brlrmr is developed implementing the proposed method and the Ibrahim and Lipsitz method.     

频带分析方法在我国景气周期波动中的应用

摘　　要:本文介绍了国外最近提出的基于频带分析的景气指标选择方法。本文首先利用带通滤波分离出我国实际GDP增长率的循环要素并把它作为基准指标,然后利用文中介绍的频带分析方法选择一致指标。在选择一致指标时发现,利用频带分析方法选择的固定资产投资指标,在时域上却未被入选。这表明选择指标时频带选择方法比时域选择方法选择范围更广。本文还分别基于频带方法和时域方法构造一致合成指数,这两者都能够较好地拟合GDP增长率的波动情况,但使用频带方法得到的结果略好一些     

重权数在复杂调查的方差估计中的应用

 方差估计是抽样调查的重要组成部分,重抽样方法是常用的方差估计方法。重权数方法与重抽样方法类似,也是利用计算机的优势通过重复获得大量不同的子样本的重权数估计目标参数的估计量和方差估计量,是一种稳健、通用、有效的方差估计方法。本文主要介绍重权数在复杂抽样调查的方差计算中的理论和应用。     

Efficient Estimation of Parameters of the Negative Binomial Distribution

In this article we investigate a class of moment-based estimators, called power method estimators, which can be almost as efficient as maximum likelihood estimators and achieve a lower asymptotic variance than the standard zero term method and method of moments estimators. We investigate different methods of implementing the power method in practice and examine the robustness and efficiency of the power method estimators.     

多边价格指数中的价格异方差问题与CPD法的改进

CPD法是国际比较项目（ICP）中应用的一种重要的多边价格比较方法,多边价格比较方法的可靠性直接影响ICP公布的各国购买力平价数据的可信度。本文从理论上系统地分析了CPD法中存在的价格异方差问题,引入一种消除异方差的WLS估计方法。然后以2011年ICP居民实际消费支出各大类商品的价格数据为例,实证说明WLS估计的CPD法能显著提高各国购买力平价数据的可靠性;相对而言,存在异方差的CPD法会系统性高估发展中国家的居民实际消费支出水平。     

Polya后验方法在有限总体抽样估计中的模拟研究

polya后验方法作为一种无信息贝叶斯估计方法,在有限总体抽样中,通过观测的样本,构造一系列的模拟总体,然后进行统计推断。通过统计模拟研究了polya后验方法估计的一些特点,并和Bootstrap方法进行比较。模拟结果显示:polya后验方法能够很好地估计总体的均值,随着样本量的增大,估计值与真值的差距越来越小。采用polya后验方法构造的置信区间区间长度较小,能够很好地覆盖真值。     

A two-stage method for the estimation of global sensitivity indices of non-parametric models

This article aims to propose a method to effectively estimate global sensitivity indices under non-parametric models. The new method involves two stages. First, all the non-influential sensitivity indices are filtered out by an adjustive W-statistic test process with low cost, and then the remaining significant sensitivity indices are precisely estimated by an orthogonal array (OA) with large number of levels and low strength. The method avoids complicated prototype building and shows a much lower experimental cost. The performance of this method as well as comparisons with polynomial regression method, Gaussian Process (GP) method, and component selection and smoothing operator (COSSO) method are tested on three numerical models that are widely used in engineering and statistical areas. Finally, a real data example is analyzed.     

空间滞后分位数回归模型的工具变量估计及参数检验

本文提出一种遵循空间数据分布特征的空间分位数回归模型,并着重探讨该模型的估计方法和参数检验问题。本文构建了上述模型的一个工具变量估计法,通过数理证明建立了估计量的大样本理论,并基于估计量的渐近分布构造了模型的参数检验方法。本文还通过数值模拟方法和应用实例考察估计方法和参数检验方法的实际应用效果,数值模拟结果显示,估计方法和参数检验方法在有限样本条件下均可以达到较高的精确度和稳定性。在应用实例中,本文利用所构建的理论方法重新检验我国“资源诅咒”效应的存在性,实证结果体现了理论方法的应用价值。     

EMPIRICAL LIKELIHOOD METHODS FOR THE GINI INDEX

The Gini index and its generalizations have been used extensively for measuring inequality and poverty in the social sciences. Recently, interval estimation based on nonparametric statistics has been proposed in the literature, for example the naive bootstrap method, the iterated bootstrap method and the bootstrap method via a pivotal statistic. In this paper, we propose empirical likelihood methods to construct confidence intervals for the Gini index or the difference of two Gini indices. Simulation studies show that the proposed empirical likelihood method performs slightly worse than the bootstrap method based on a pivotal statistic in terms of coverage accuracy, but it requires less computation. However, the bootstrap calibration of the empirical likelihood method performs better than the bootstrap method based on a pivotal statistic.     

Parameter estimation for generalized Pareto distribution by generalized probability weighted moment-equations

The generalized Pareto distribution (GPD) has been widely used to model exceedances over a threshold. This article generalizes the method of generalized probability weighted moments, and applies this method to estimate the parameters of GPD. The estimator is computationally easy. Some asymptotic results of this method are provided. Two simulations are carried out to investigate the behavior of this method and to compare them with other methods suggested in the literature. The simulation results show that the performance of the proposed method is better than some other methods. Finally, this method is applied to analyze a real-life data.     

Nonparametric confidence intervals for Tmax in sequence-stratified crossover studies

Tmax is the time associated with the maximum serum or plasma drug concentration achieved following a dose. While Tmax is continuous in theory, it is usually discrete in practice because it is equated to a nominal sampling time in the noncompartmental pharmacokinetics approach. For a 2-treatment crossover design, a Hodges-Lehmann method exists for a confidence interval on treatment differences. For appropriately designed crossover studies with more than two treatments, a new median-scaling method is proposed to obtain estimates and confidence intervals for treatment effects. A simulation study was done comparing this new method with two previously described rank-based nonparametric methods, a stratified ranks method and a signed ranks method due to Ohrvik. The Normal theory, a nonparametric confidence interval approach without adjustment for periods, and a nonparametric bootstrap method were also compared. Results show that less dense sampling and period effects cause increases in confidence interval length. The Normal theory method can be liberal (i.e. less than nominal coverage) if there is a true treatment effect. The nonparametric methods tend to be conservative with regard to coverage probability and among them the median-scaling method is least conservative and has shortest confidence intervals. The stratified ranks method was the most conservative and had very long confidence intervals. The bootstrap method was generally less conservative than the median-scaling method, but it tended to have longer confidence intervals. Overall, the median-scaling method had the best combination of coverage and confidence interval length. All methods performed adequately with respect to bias.     

On estimating the dimensionality in discriminant analysis

In discriminant analysis, the dimension of the hyperplane which population mean vectors span is called the dimensionality. The procedures commonly used to estimate this dimension involve testing a sequence of dimensionality hypotheses as well as model fitting approaches based on (consistent) Akaike's method, (modified) Mallows' method and Schwarz's method. The marginal log-likelihood (MLL) method is developed and the asymptotic distribution of the dimensionality estimated by this method for normal populations is derived. Furthermore a modified marginal log-likelihood (MMLL) method is also considered. The MLL method is not consistent for large samples and two modified criteria are proposed which attain asymptotic consistency. Some comments are made with regard to the robustness of this method to departures from normality. The operating characteristics of the various methods proposed are examined and compared.     

Seasonal Adjustment Using Structural Time Series Models: An Application and a Comparison With the Census X-11 Method

This article makes the method of seasonal adjustment operational using suitable structural time series models (STM). This so-called STM method is applied to several relevant Dutch macro- economic quarterly and monthly time series. The results are compared with those of the Census X-11 method using several formal criteria as yardsticks. The STM method proves to compete well with the Census X-11 method in this respect.     

Sparse canonical correlation analysis between an alcohol biomarker and self-reported alcohol consumption

In investigating the correlation between an alcohol biomarker and self-report, we developed a method to estimate the canonical correlation between two high-dimensional random vectors with a small sample size. In reviewing the relevant literature, we found that our method is somewhat similar to an existing method, but that the existing method has been criticized as lacking theoretical grounding in comparison with an alternative approach. We provide theoretical and empirical grounding for our method, and we customize it for our application to produce a novel method, which selects linear combinations that are step functions with a sparse number of steps.     

Semiparametric inference with a functional-form empirical likelihood

A functional-form empirical likelihood method is proposed as an alternative method to the empirical likelihood method. The proposed method has the same asymptotic properties as the empirical likelihood method but has more flexibility in choosing the weight construction. Because it enjoys the likelihood-based interpretation, the profile likelihood ratio test can easily be constructed with a chi-square limiting distribution. Some computational details are also discussed, and results from finite-sample simulation studies are presented.     

概化理论方差分量及其变异量估计:Jackknife方法与Traditional方法比较

文章生成概化理论p×i、p×i×h、p×(i:h)三种不同设计下的正态数据、多项数据和二项数据,用Jackknife方法和Traditional方法估计数据的方差分量、标准误和置信区间,并比较这两种方法的性能。结果表明:(1)Jackknife方法在方差分量估计和标准误估计上都较为准确;(2)相较于Traditional方法,Jackknife方法在方差分量置信区间估计上略有不足。(3)相较于Traditional方法,Jackknife方法估计的准确性不随数据类型、研究设计和方差分量的不同而产生波动,具有更强的稳健性。