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.     

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.     

渐消记忆型自适应线性回归模型参数估计算法及应用

本文讨论了根据经济信息的不断更新,调整原有线性回归模型的必要性,介绍了一种渐消记忆型自适应线性回归模型参数估计算法,并给出了应用实例.     

基于马尔可夫状态转换模型的沪深股市波动率的估计

为了更准确地估计具有结构转换的沪深股市收益率波动特征,本文将沪深股市的波动变化分为上涨、下跌和盘整三个状态,选用2000年1月4日至2011年12月30日的上证综指和深证成指日收益率数据作为样本,2012年1月4日至2012年1月17日的日收益率作为样本外预测,分别应用GARCH和APGARCH模型,以及RS-GARCH和RS-APGARCH模型估计和预测两序列的波动率,最后采用MSE₁、MSE₂和QLIKE对估计和预测出的波动率进行评价。结果表明:单一状态和三种状态下APGARCH模型均比GARCH模型估计和预测的波动率更准确;更进一步带有马尔可夫状态转换的模型估计和预测出的波动率更准确,且误差分布服从正态分布的模型估计和预测的波动率拟合结果优于误差服从t分布的模型。     

Estimation When a Parameter is on a Boundary

This paper establishes the asymptotic distribution of an extremum estimator when the true parameter lies on the boundary of the parameter space. The boundary may be linear, curved, and/or kinked. Typically the asymptotic distribution is a function of a multivariate normal distribution in models without stochastic trends and a function of a multivariate Brownian motion in models with stochastic trends. The results apply to a wide variety of estimators and models. Examples treated in the paper are: (i) quasi-ML estimation of a random coefficients regression model with some coefficient variances equal to zero and (ii) LS estimation of an augmented Dickey-Fuller regression with unit root and time trend parameters on the boundary of the parameter space.     

Identification and Estimation of Regression Models with Misclassification

This paper studies the problem of identification and estimation in nonparametric regression models with a misclassified binary regressor where the measurement error may be correlated with the regressors. We show that the regression function is nonparametrically identified in the presence of an additional random variable that is correlated with the unobserved true underlying variable but unrelated to the measurement error. Identification for semiparametric and parametric regression functions follows straightforwardly from the basic identification result. We propose a kernel estimator based on the identification strategy, derive its large sample properties, and discuss alternative estimation procedures. We also propose a test for misclassification in the model based on an exclusion restriction that is straightforward to implement.     

基于TDAR模型的VaR估计方法及应用

文献中, 在险值估计方法一般基于线性假设, 但是该假设在实际中很难满足,需要为此提出非线性的在险值估计方法。与以往传统模型一般假定变化发生在"时间"点上不同,门限双自回归 (TDAR)因状态空间的不同而建立不同模型来对非对称性、结构变点等非线性现象进行刻画,并同时允许均值和波动率过程的结构变化。本文首次基于TDAR建立TDAR-VaR方法,并对上证指数和香港恒生指数进行了实证研究和对杠杆效应进行了分析。实证分析发现TDAR-VaR较好地预测了市场风险。     

Risk perception and the economic crisis: a longitudinal study of the trajectory of perceived risk

We conducted a longitudinal survey of public response to the economic crisis to understand the trajectory of risk perception amidst an ongoing crisis. A nation-wide panel responded to seven surveys beginning in late September 2008 at the peak of the crisis and concluded in October 2009. At least 600 respondents participated in each survey, with 413 completing all seven surveys. Our online survey focused on perceptions of risk (savings, investments, retirement, job), negative emotions toward the financial crisis (sadness, anxiety, fear, anger, worry, stress), confidence in national leaders to manage the crisis (President Obama, Congress, Treasury Secretary, business leaders), and belief in one's ability to realize personal objectives despite the crisis. We employed latent growth curve modeling to analyze change in risk perception throughout the crisis. Our results suggest that, in general, people's perceptions of risk appear to decrease most rapidly during the initial phase of a crisis and then begin to level off. Negative emotion about the crisis was the most predictive of increased risk perception, supporting the notion of risk as feelings. Belief in one's ability to realize personal objectives was also predictive. Confidence in national leaders, however, was not predictive of perceived risk. Finally, our results demonstrate that groups may experience a crisis differently depending on a combination of personal characteristics such as gender, income, numeracy, and political attitude. Risk management and communication should work in sync with these mechanisms and differences across groups.     

A Universal Appointment Rule in the Presence of No‐Shows and Walk‐Ins

This study introduces a universal “Dome” appointment rule that can be parameterized through a planning constant for different clinics characterized by the environmental factors—no‐shows, walk‐ins, number of appointments per session, variability of service times, and cost of doctor's time to patients’ time. Simulation and nonlinear regression are used to derive an equation to predict the planning constant as a function of the environmental factors. We also introduce an adjustment procedure for appointment systems to explicitly minimize the disruptive effects of no‐shows and walk‐ins. The procedure adjusts the mean and standard deviation of service times based on the expected probabilities of no‐shows and walk‐ins for a given target number of patients to be served, and it is thus relevant for any appointment rule that uses the mean and standard deviation of service times to construct an appointment schedule. The results show that our Dome rule with the adjustment procedure performs better than the traditional rules in the literature, with a lower total system cost calculated as a weighted sum of patients’ waiting time, doctor's idle time, and doctor's overtime. An open‐source decision‐support tool is also provided so that healthcare managers can easily develop appointment schedules for their clinical environment.     

Efficient Semiparametric Estimation of Censored and Truncated Regressions via a Smoothed Self‐Consistency Equation

An asymptotically efficient likelihood‐based semiparametric estimator is derived for the censored regression (tobit) model, based on a new approach for estimating the density function of the residuals in a partially observed regression. Smoothing the self‐consistency equation for the nonparametric maximum likelihood estimator of the distribution of the residuals yields an integral equation, which in some cases can be solved explicitly. The resulting estimated density is smooth enough to be used in a practical implementation of the profile likelihood estimator, but is sufficiently close to the nonparametric maximum likelihood estimator to allow estimation of the semiparametric efficient score. The parameter estimates obtained by solving the estimated score equations are then asymptotically efficient. A summary of analogous results for truncated regression is also given.     

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。     

Efficient Semiparametric Estimation of the Fama–French Model and Extensions

This paper develops a new estimation procedure for characteristic‐based factor models of stock returns. We treat the factor model as a weighted additive nonparametric regression model, with the factor returns serving as time‐varying weights and a set of univariate nonparametric functions relating security characteristic to the associated factor betas. We use a time‐series and cross‐sectional pooled weighted additive nonparametric regression methodology to simultaneously estimate the factor returns and characteristic‐beta functions. By avoiding the curse of dimensionality, our methodology allows for a larger number of factors than existing semiparametric methods. We apply the technique to the three‐factor Fama–French model, Carhart's four‐factor extension of it that adds a momentum factor, and a five‐factor extension that adds an own‐volatility factor. We find that momentum and own‐volatility factors are at least as important, if not more important, than size and value in explaining equity return comovements. We test the multifactor beta pricing theory against a general alternative using a new nonparametric test.     

生猪产业链价格的区制转移与非线性动态调整行为研究

准确刻画生猪产业链价格的动态行为特征是预测生猪市场价格变化趋势的基础,也是政府制定切实可行的生猪价格稳定政策的重要依据。本文以1994年6月至2018年6月中国生猪价格和猪肉价格月度数据为研究对象,运用多区制平滑转移自回归模型划分了生猪价格和猪肉价格序列的区制并实证分析了其区制转移特征;结合多区制平滑转移自回归估计方程的特征根和非线性脉冲响应分析进一步探究了生猪价格和猪肉价格的非线性动态调整行为特征。研究结果表明,生猪价格遵从一个三区制的平滑转移过程,猪肉价格则为一个四区制的平滑转移过程。生猪价格仅在低价位区和高价位区具有较强的持续性,而猪肉价格则在价格低迷区、价格坚挺区和价格恢复区均具有较强的持续性。从区制转移路径上来看,生猪价格很容易从中等价位迅速暴跌到低价位,猪肉价格倾向于由价格下跌区向价格低迷区转移以及由价格恢复区向价格坚挺区转移。随机冲击对猪肉价格的影响持久且具有微弱的"自我调节机制",对生猪价格的影响具有持久性但缺乏"自我调节机制";短期内,随机冲击对生猪市场价格波动起到正向促进作用,在长期,随机冲击对生猪价格主要体现为负效应,对猪肉价格整体表现为正效应。     

Estimation and Inference With Weak,Semi‐Strong,and Strong Identification

This paper analyzes the properties of standard estimators, tests, and confidence sets (CS's) for parameters that are unidentified or weakly identified in some parts of the parameter space. The paper also introduces methods to make the tests and CS's robust to such identification problems. The results apply to a class of extremum estimators and corresponding tests and CS's that are based on criterion functions that satisfy certain asymptotic stochastic quadratic expansions and that depend on the parameter that determines the strength of identification. This covers a class of models estimated using maximum likelihood (ML), least squares (LS), quantile, generalized method of moments, generalized empirical likelihood, minimum distance, and semi‐parametric estimators. The consistency/lack‐of‐consistency and asymptotic distributions of the estimators are established under a full range of drifting sequences of true distributions. The asymptotic sizes (in a uniform sense) of standard and identification‐robust tests and CS's are established. The results are applied to the ARMA(1, 1) time series model estimated by ML and to the nonlinear regression model estimated by LS. In companion papers, the results are applied to a number of other models.     

Growth and Heat Resistance Kinetic Variation Among Various Isolates of Salmonella and its Application to Risk Assessment

The abilities of cells of a particular type of bacteria to leave lag phase and begin the process of dividing or surviving heat treatment can depend on the serotypes or strains of the bacteria. This article reports an investigation of serotype-specific differences in growth and heat resistance kinetics of clinical and food isolates of Salmonella. Growth kinetics at 19 degrees C and 37 degrees C were examined in brain heart infusion broth and heat resistance kinetics for 60 degrees C were examined in beef gravy using a submerged coil heating apparatus. Estimates of the parameters of the growth curves suggests a small between-serotype variance of the growth kinetics. However, for inactivation, the results suggest a significant between-serotype effect on the asymptotic D-values, with an estimated between-serotype CV of about 20%. In microbial risk assessment, predictive microbiology is used to estimate growth and inactivation of pathogens. Often the data used for estimating the growth or inactivation kinetics are based on measurements on a cocktail--a mixture of approximately equal proportions of several serotypes or strains of the pathogen being studied. The expected growth or inactivation rates derived from data using cocktails are biased, reflecting the characteristics of the fastest growing or most heat resistant serotype of the cocktail. In this article, an adjustment to decrease this possible bias in a risk assessment is offered. The article also presents discussion of the effect on estimating growth when stochastic assumptions are incorporated in the model. In particular, equations describing the variation of relative growth are derived, accounting for the stochastic variations of the division of cells. For small numbers of cells, the expected value of the relative growth is not an appropriate "representative" value for actual relative growths that might occur.     

Robust Estimation and Inference for Jumps in Noisy High Frequency Data: A Local‐to‐Continuity Theory for the Pre‐Averaging Method

We develop an asymptotic theory for the pre‐averaging estimator when asset price jumps are weakly identified, here modeled as local to zero. The theory unifies the conventional asymptotic theory for continuous and discontinuous semimartingales as two polar cases with a continuum of local asymptotics, and explains the breakdown of the conventional procedures under weak identification. We propose simple bias‐corrected estimators for jump power variations, and construct robust confidence sets with valid asymptotic size in a uniform sense. The method is also robust to certain forms of microstructure noise.     

金融网络风险下多因子矩阵回归的资产组合与定价

本文从企业的股权、债权关系出发,基于违约距离构建无向图网络,分析了不确定性风险以网络形式进行传染、溢出和蔓延等现象,通过最小生成树的稀疏网络优化方法最大限度降低资产组合的非线性风险影响。站在资源配置的角度,利用稀疏聚类算法深入挖掘资产特征和捕捉其间的相依关系,采用多目标、多指数的稳健矩阵回归策略动态跟踪市场趋势,并通过自适应权重学习策略对网络风险叠加影响下的资产组合进行选择和配置,最终获得最小生成树风险下投资组合的稀疏聚类优化策略,进一步扩充了资产定价多因子模型。研究发现多目标矩阵回归的稀疏聚类投资组合,不仅对组合内投资标的进行了选择性舍弃,使资金能够集中配置于优质资产,更有助于通过最小生成树减缓甚至切断风险在网络中的传播,有效降低了资产之间风险的传染性。基于金融网络的风险分析方法不仅有效地刻画了风险以网络方式互相传染、互相影响、互相强化的非线性叠加效应,而且通过资产之间配置系数的压缩变换和最小生成树的优化方式,最小化最坏情形下风险传染的影响,对复杂网络环境下的资产配置和全面风险管理进行了有益补充,为长期投资基金获得风险和收益更为均衡的资产配置,提供了合意的投资策略和决策依据。     

基于MSVAR进出口贸易与经济增长的非线性效应分析

运用MSVAR模型分析1952年～2007年中国进、出口贸易和经济增长之间的关系,应用bootstrap仿真实验检验模型系数的显著性,利用状态相依广义脉冲响应函数简化解释MSVAR模型变量之间的关系。研究结果表明,该方法能准确捕捉对外贸易和经济增长关系的状态转换的时点,可以明确地确定两种状态,即国内或者国际环境不稳定,不利于发展对外贸易的状态1;国内和国际环境稳定,有利于发展对外贸易的状态2。进出口增长率与GDP增长率存在动态非线性关系,在国内和国际环境有利于发展对外贸易的稳定状态下,进、出口增长率的冲击对经济增长有较长期的正效应;在国内和国际环境不利于发展对外贸易的不稳定状态下,进、出口增长率的冲击对经济增长有负影响,并且具有比稳定状态时冲击影响更大、延续期短的特征。进出口贸易在不同的经济环境下影响经济增长的效应不同。     

TEI@I方法论及其在外汇汇率预测中的应用

基于TE I@I方法论的理论框架,构建了一个基于TE I@I方法论的外汇汇率预测模型。在此模型中,传统的经济计量模型用于处理外汇汇率的主要趋势,人工神经网络技术用于分析外汇汇率的非线性,而文本挖掘和专家系统用于处理外汇市场中的突现性和不稳定性。最后,基于集成的思想,利用支持向量回归技术对上述3个部分进行非线性集成,从而获得一个更为精确的预测结果。通过实证方法验证了基于TE I@I方法论的外汇汇率预测模型的有效性。