Nonparametric Instrumental Variable Estimation of Structural Quantile Effects

We study the asymptotic distribution of Tikhonov regularized estimation of quantile structural effects implied by a nonseparable model. The nonparametric instrumental variable estimator is based on a minimum distance principle. We show that the minimum distance problem without regularization is locally ill‐posed, and we consider penalization by the norms of the parameter and its derivatives. We derive pointwise asymptotic normality and develop a consistent estimator of the asymptotic variance. We study the small sample properties via simulation results and provide an empirical illustration of estimation of nonlinear pricing curves for telecommunications services in the United States.     

Identification of Marginal Effects in Nonseparable Models Without Monotonicity

Nonseparable models do not impose any type of additivity between the unobserved part and the observable regressors, and are therefore ideal for many economic applications. To identify these models using the entire joint distribution of the data as summarized in regression quantiles, monotonicity in unobservables has frequently been assumed. This paper establishes that in the absence of monotonicity, the quantiles identify local average structural derivatives of nonseparable models.     

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。     

基于零膨胀分位数两部模型的银行贷款违约预测研究

贷款信用风险评估是银行风控的重要内容。贷款逾期天数作为常见的风险度量指标,具有典型的零膨胀特征。对于零膨胀数据,传统的线性回归不再适用,两部模型是常用的代表方法。考虑到贷款数据具有偏态分布特征,本文构建了一个分位数两部模型—logit-quantile模型。该模型由Logistic回归和分位数回归构成,为了进行风险因素的选择,在模型的两个回归中添加了Lasso惩罚。为了求解模型,本文采用了坐标下降法和线性规划法相结合的迭代算法。模拟分析显示,对比逐步法和常用的logit-linear两部模型,新模型表现出了最好的变量选择效果,尤其在零膨胀比例为80%及高维情形时,该模型的表现仍然最优。最后对某银行的贷款数据实证分析显示,新模型具有更精简的结构,采用交叉验证技术进行预测显示新模型的预测和分类表现最好。     

Nonparametric Instrumental Variables Estimation of a Quantile Regression Model

We consider nonparametric estimation of a regression function that is identified by requiring a specified quantile of the regression “error” conditional on an instrumental variable to be zero. The resulting estimating equation is a nonlinear integral equation of the first kind, which generates an ill‐posed inverse problem. The integral operator and distribution of the instrumental variable are unknown and must be estimated nonparametrically. We show that the estimator is mean‐square consistent, derive its rate of convergence in probability, and give conditions under which this rate is optimal in a minimax sense. The results of Monte Carlo experiments show that the estimator behaves well in finite samples.     

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。     

加权复合分位数回归方法在动态VaR风险度量中的应用

风险价值(VaR)因为简单直观,成为了当今国际上最主流的风险度量方法之一,而基于时间序列自回归(AR)模型来计算无条件风险度量值在实业界有广泛应用。本文基于分位数回归理论对AR模型提出了一个估计方法--加权复合分位数回归(WCQR)估计,该方法可以充分利用多个分位数信息提高参数估计的效率,并且对于不同的分位数回归赋予不同的权重,使得估计更加有效,文中给出了该估计的渐近正态性质。有限样本的数值模拟表明,当残差服从非正态分布时,WCQR估计的的统计性质接近于极大似然估计,而该估计是不需要知道残差分布的,因此,所提出的WCQR估计更加具有竞争力。此方法在预测资产收益的VaR动态风险时有较好的应用,我们将所提出的理论分析了我国九只封闭式基金,实证分析发现,结合WCQR方法求得的VaR风险与用非参数方法求得的VaR风险非常接近,而结合WCQR方法可以计算动态的VaR风险值和预测资产收益的VaR风险值。     

Nonparametric Identification under Discrete Variation

This paper provides weak conditions under which there is nonparametric interval identification of local features of a structural function that depends on a discrete endogenous variable and is nonseparable in latent variates. The function delivers values of a discrete or continuous outcome and instruments may be discrete valued. Application of the analog principle leads to quantile regression based interval estimators of values and partial differences of structural functions. The results are used to investigate the nonparametric identifying power of the quarter‐of‐birth instruments used in Angrist and Krueger's 1991 study of the returns to schooling.     

基于虚拟变量分位点回归模型的条件VaR估计以及杠杆效应分析

已有成果在研究杠杆效应时大多数都是基于ARCH类模型,从波动率的角度进行分析的。本文应用分位点回归模型以及含有虚拟变量的分位点回归模型分析了"已实现"波动率条件下的CVaR,并尝试从市场风险的角度对杠杆效应进行分析。最后,对中国股票市场进行了实证研究,得到了"已实现"波动率条件下的CVaR估计,并从风险的角度证实了中国股市的市场风险存在杠杆效应。     

比例数据基于Tobit分位数回归模型的贝叶斯变量选择

针对家庭商业健康保险参保比例在[0,1]闭区间上取值的特点,本文基于Tobit模型给出了比例响应数据的贝叶斯分位数回归建模方法。通过引入回归系数的“Spike-and-slab”先验分布,应用EM算法我们提出了基于门限规则的贝叶斯变量选择方法。大量数值模拟研究验证了所提的贝叶斯变量选择方法的有效性,且具有易操作、计算量小等优点。最后,将此方法应用到家庭商业健康保险数据的实证分析,研究不同分位数水平下家庭健康保险参保比例的影响因素,得到了许多有意义的研究结果。     

指令不均衡与股票收益关系研究——基于大规模数据分位数回归的实证

在指令不均衡与股票收益关系研究中,常常遇到两个困难：第一,不同市场环境下,前者对后者存在异质影响;第二,往往涉及大规模数据处理。为此,运用大规模数据分位数回归的方法,一方面揭示不同分位点处指令不均衡对股票收益的异质影响,细致刻画两者之间关系;另一方面适应大规模数据建模要求,得到更为可靠的结果。以上证A股和深证A股为研究对象,通过大规模数据分位数回归方法,得到了比均值回归更多有用信息。实证结果表明：第一,在高分位点处,滞后1期指令不均衡对股票收益具有正向影响且呈现上升趋势,而在低分位点却具有负向影响;第二,控制当期指令不均衡后,滞后期指令不均衡对股票收益具有负向影响,且随着分位点的增加呈现下降趋势。这些结果意味着,指令不均衡对股票收益具有一定的解释能力和预测能力。     

Instrumental Variable Treatment of Nonclassical Measurement Error Models

While the literature on nonclassical measurement error traditionally relies on the availability of an auxiliary data set containing correctly measured observations, we establish that the availability of instruments enables the identification of a large class of nonclassical nonlinear errors‐in‐variables models with continuously distributed variables. Our main identifying assumption is that, conditional on the value of the true regressors, some “measure of location” of the distribution of the measurement error (e.g., its mean, mode, or median) is equal to zero. The proposed approach relies on the eigenvalue–eigenfunction decomposition of an integral operator associated with specific joint probability densities. The main identifying assumption is used to “index” the eigenfunctions so that the decomposition is unique. We propose a convenient sieve‐based estimator, derive its asymptotic properties, and investigate its finite‐sample behavior through Monte Carlo simulations.     

IV Quantile Regression for Group‐Level Treatments,With an Application to the Distributional Effects of Trade

We present a methodology for estimating the distributional effects of an endogenous treatment that varies at the group level when there are group‐level unobservables, a quantile extension of Hausman and Taylor, 1981. Because of the presence of group‐level unobservables, standard quantile regression techniques are inconsistent in our setting even if the treatment is independent of unobservables. In contrast, our estimation technique is consistent as well as computationally simple, consisting of group‐by‐group quantile regression followed by two‐stage least squares. Using the Bahadur representation of quantile estimators, we derive weak conditions on the growth of the number of observations per group that are sufficient for consistency and asymptotic zero‐mean normality of our estimator. As in Hausman and Taylor, 1981, micro‐level covariates can be used as internal instruments for the endogenous group‐level treatment if they satisfy relevance and exogeneity conditions. Our approach applies to a broad range of settings including labor, public finance, industrial organization, urban economics, and development; we illustrate its usefulness with several such examples. Finally, an empirical application of our estimator finds that low‐wage earners in the United States from 1990 to 2007 were significantly more affected by increased Chinese import competition than high‐wage earners.     

利率期限结构模型非线性建模

应用门限模型对利率期限结构模型中漂移项的非线性性进行建模,提出门限(threshold)CKLS模型。用基于自助法(bootstrap)的广义拟似然比检验方法对门限CKLS模型进行了检验。检验结论表明:门限CKLS模型能较好的刻画利率期限结构模型中漂移项的非线性,在0.1的显著水平下优于CKLS模型。     

The Nonparametric Identification of Treatment Effects in Duration Models

This paper analyzes the specification and identification of causal multivariate duration models. We focus on the case in which one duration concerns the point in time a treatment is initiated and we are interested in the effect of this treatment on some outcome duration. We define “no anticipation of treatment” and relate it to a common assumption in biostatistics. We show that (i) no anticipation and (ii) randomized treatment assignment can be imposed without restricting the observational data. We impose (i) but not (ii) and prove identification of models that impose some structure. We allow for dependent unobserved heterogeneity and we do not exploit exclusion restrictions on covariates. We provide results for both single‐spell and multiple‐spell data. The timing of events conveys useful information on the treatment effect.     

对于尝试-重购新产品扩散模型的改进：logit模型及NILS估计

在现有的尝试-重购模型的基础上,本文构建了一个更加合理的logit形式的尝试-重购模型。发展出一套适用于该模型的估计方法和检验方法,并用Monte Carlo随机模拟实验对此方法的有效性进行验证。经检验,随着样本数据量的增加和样本标准差的减小,无论是模型参数的估计误差,还是单参数显著性检验的效力,以及犯第一类错误的可能性都表现出合理的变化趋势。该模型可以用于快速消费品新产品的销量预测和营销组合分析。     

我国R&D强度的影响因素——基于局部调整模型的实证检验

采用1991～2007年时间序列数据实证研究了影响我国R&D强度的因素。首先从理论上分析了影响R&D强度的5个因素:企业因素、政策因素、公共R&D部门、投资因素、经济因素;根据数据的可获得性设计了企业R&D经费投入强度、企业R&D人员投入强度、行业结构、政府直接R&D补贴、知识产权保护、公共研发部门R&D支出的力度、固定资产投资、人力资本投资、人均GDP、GDP增长率、外贸依存度、外商直接投资12个变量。以局部调整模型为基础设计了实证模型,通过分析实证结果,发现企业R&D经费投入强度、行业结构、知识产权保护和固定资产投资等因素对R&D强度具有显著的正向影响。     

补货能力影响部分短缺量拖后率的边补货边需求EOQ模型

Based on the hypothesis of time-dependent partial backlogging,the effect of the waiting time between the next replenishment and the satisfaction of the backlogging demand on the rate of partial backlog-grog was investigated in this paper. Then the backlogging rate influenced by the replenishment capacity and the total of backlogging demand was proposed. Based on this new standpoint,an EOQ model of replenishment with the replenishment capacity affecting the partial backlogging was built up.At last,an emulator was performed and the results showed:improving the replenishment capacity could reduce the replenishment times to a lower constant level;this effect may increase the waiting time before the next replenishment,but it could decrease the waiting time after the start of reple山shment;so the trend of the lostsales varying with the replenishment capacity was not very distinct at the medium level of the replenishment capacity;However,the more adequate replenishment capacity could reduce the number of lostsales and improve the rate of partial backlogging during shortage period than the less one.     

中国普惠金融对产业结构的非线性影响：基于PSTR的实证分析

作为中国经济政策改革的一项重要措施,普惠金融旨在有效、全方位地为社会所有阶层和群体提供金融服务,对包括产业结构在内的诸多经济指标的发展具有重要的影响。本文基于Sarma(2008)提出的普惠金融指标体系,测算了2005-2017年我国31个省市自治区的普惠金融指数,在此基础上,运用面板平滑转换(PSTR)模型探究普惠金融对产业结构合理化和高级化的非线性影响。研究表明,样本期内我国普惠金融发展水平呈现区域性差异,东部地区的普惠金融指数明显高于中西部地区。普惠金融的发展显著降低了产业结构偏离经济均衡状态的程度,对产业结构合理化和高级化有显著的促进作用,且这种作用表现出显著的非线性特征。当普惠金融指数大于0.2402时,普惠金融的发展对产业结构合理化的促进作用逐渐加强;当普惠金融指数大于0.5914时,普惠金融的发展对产业结构高级化的促进作用明显加强。同时,普惠金融对产业结构升级的影响存在区域异质性,普惠金融对东部、中部地区的产业结构升级有显著的促进作用,但对西部地区无显著影响。此外,本文采用系统GMM方法对模型进行稳健性检验,结论仍然成立。