Robust analogs to the coefficient of variation

The coefficient of variation (CV) is commonly used to measure relative dispersion. However, since it is based on the sample mean and standard deviation, outliers can adversely affect it. Additionally, for skewed distributions the mean and standard deviation may be difficult to interpret and, consequently, that may also be the case for the CV. Here we investigate the extent to which quantile-based measures of relative dispersion can provide appropriate summary information as an alternative to the CV. In particular, we investigate two measures, the first being the interquartile range (in lieu of the standard deviation), divided by the median (in lieu of the mean), and the second being the median absolute deviation, divided by the median, as robust estimators of relative dispersion. In addition to comparing the influence functions of the competing estimators and their asymptotic biases and variances, we compare interval estimators using simulation studies to assess coverage.     

农业生产者绿色生产要素投入行为的收入效应

以有机肥和生物农药两类绿色投入品为例,运用内生转换回归模型,实证分析农业生产者绿色生产要素投入行为的收入效应,并运用无条件分位数回归进行异质性分析。研究表明:(1)绿色生产要素投入行为受到内外部因素的影响,不同绿色生产要素的投入行为影响因素有所差异;(2)不同绿色生产要素投入决策条件下农业收入的影响因素有较大差异,新型农业经营类型能够显著提高农业收入;(3)绿色生产要素投入行为具有正向收入效应;(4)有机肥投入随着农业收入的提升,影响程度有逐渐递减趋势,生物农药投入对农业收入的影响程度呈现“倒U”型特征,不同主体类型和地理区域对不同农业收入水平的影响程度存在显著差异。     

石油期货收益率的分位数建模及其影响因素分析

石油期货收益率的分位数反映了收益率分布特征和石油市场风险特征,有必要建模考察分位数的变化模式与影响因素。针对现有研究在模型方法和分析角度上的不足,本文考虑分位数受市场冲击影响而产生的非线性自回归特征,提出门限CAViaR模型并用以分析石油期货收益率的分位数及其影响因素。基于1998-2009年布伦特原油期货价格的研究表明,石油期货收益率的分位数具有自回归特征并受前期油价涨跌的不对称影响,且油价下跌的作用更强。左尾分位数受油价涨跌的共同影响,而右尾分位数仅受油价下跌的影响,二者呈现不同特征。此外,本文通过考察分位数的动态变化模式揭示了油价风险特征,具有重要的风险管理作用。     

上市公司股权再融资与业绩变化

采用中国沪深股市1999年-2006年上市公司面板数据,运用分位回归方法,研究上市公司股权再融资后的业绩变化,探讨业绩处于不同分位点的上市公司股权再融资行为对业绩影响的差异性.研究结果显示,从1999年～2006年上市公司经营业绩的下滑是整体性的.通过在模型中引入股权再融资虚拟变量,基于分年度的OLS回归表明,在2000年实施股权再融资的上市公司与在研究期内没有股权再融资行为的上市公司相比,经营业绩的下滑更严重;处于高分住点的股权再融资样本公司不同于其他公司,它们的业绩变化与股权再融资并没有显著相关性,这表明股权再融资后公司的业绩变化存在内部差异性,同时在各个分位点上市公司的规模增加均对经营业绩具有促进作用,负债率对业绩改善存在负面影响.     

基于非线性分位数回归模型的多期VaR风险测度

多期VaR主要受到持有期及波动率两个变量的影响,并且其影响模式(线性或非线性)的确定对于准确地进行VaR风险测度至关重要。非线性分位数回归模型,能够克服线性分位数回归模型只能揭示多期VaR及其影响因素之间线性依赖关系的局限,从而提高多期VaR风险测度的准确性。结合波动模型与两个非线性分位数回归方法:QRNN和SVQR,给出了多期VaR风险测度的三类方案:波动模型法、QRNN+波动模型法、SVQR+波动模型法。选取3个股票价格指数作为研究对象,考虑了6种不同形式的波动模型,得到了18个多期VaR风险测度方法进行实证比较,结果表明:波动模型选择影响到多期VaR风险测度效果;SVQR+波动模型法略优于QRNN+波动模型法,并且两者显著优于波动模型法。     

基于MIDAS分位数回归的条件偏度组合投资决策

条件偏度是金融市场典型特征之一,忽略条件偏度的组合投资决策往往难以有效地分散金融风险。为此,本文构建了包含条件偏度的组合投资模型,并给出其建模方法。首先,运用MIDAS-QR模型,改善条件偏度测度效果;其次,基于CRRA效用函数,将组合投资权重设计为条件偏度和特征变量的线性组合,建立组合投资模型并给出求解方案;最后,从沪深300指数中选取10支代表性成分股进行实证研究,从收益、风险和Sharpe比率等方面,将包含条件偏度的组合投资模型与等权方案、均值-方差模型等进行比较,分析条件偏度在组合投资中的作用。实证结果表明：MIDAS-QR是测度条件偏度的有效方法,其测度结果受异常值影响小,表现稳定;条件偏度对组合投资决策具有显著影响,包含条件偏度的组合投资模型能够有效地降低投资风险、带来更高的风险调整收益。     

Doubly robust weighted composite quantile regression based on SCAD-L2

In this article, a robust variable selection procedure based on the weighted composite quantile regression (WCQR) is proposed. Compared with the composite quantile regression (CQR), WCQR is robust to heavy-tailed errors and outliers in the explanatory variables. For the choice of the weights in the WCQR, we employ a weighting scheme based on the principal component method. To select variables with grouping effect, we consider WCQR with SCAD-L₂ penalization. Furthermore, under some suitable assumptions, the theoretical properties, including the consistency and oracle property of the estimator, are established with a diverging number of parameters. In addition, we study the numerical performance of the proposed method in the case of ultrahigh-dimensional data. Simulation studies and real examples are provided to demonstrate the superiority of our method over the CQR method when there are outliers in the explanatory variables and/or the random error is from a heavy-tailed distribution.     

Multiply robust matching estimators of average and quantile treatment effects

Propensity score matching has been a long-standing tradition for handling confounding in causal inference, however, requiring stringent model assumptions. In this article, we propose novel double score matching (DSM) utilizing both the propensity score and prognostic score. To gain the protection of possible model misspecification, we posit multiple candidate models for each score. We show that the debiasing DSM estimator achieves the multiple robustness property in that it is consistent if any one of the score models is correctly specified. We characterize the asymptotic distribution for the DSM estimator requiring only one correct model specification based on the martingale representations of the matching estimators and theory for local normal experiments. We also provide a two-stage replication method for variance estimation and extend DSM for quantile estimation. Simulation demonstrates DSM outperforms single-score matching and prevailing multiply robust weighting estimators in the presence of extreme propensity scores.     

Modeling immunogenecity data to establish screening bioassays cut point

The response of immunogenecity anti-drug antibody (ADA) generally includes biological and analytical variability. The nature of biological and analytical variations may lead to a variety of symmetric and asymmetric ADA data. As a result, current statistical methods may yield unreliable results because these methods assume special types of symmetric or asymmetric ADA data. In this paper, we survey and compare parametric models that are useful for analyzing a variety of asymmetric data that have rarely been used to calculate assay cut points. These models include symmetric distributions as limiting case; therefore, they are useful in the analysis of a variety of symmetric data. We also investigate two nonparametric approaches that have received little attention in screening cut point calculations. A simulation study was conducted to compare the performance of the methods. We evaluate the methods using four published different types of data, and make recommendations concerning the use of the methods.