Identification and Estimation of Stochastic Bargaining Models

Stochastic sequential bargaining models (Merlo and Wilson (1995, 1998)) have found wide applications in different fields including political economy and macroeconomics due to their flexibility in explaining delays in reaching an agreement. This paper presents new results in nonparametric identification and estimation of such models under different data scenarios.     

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.     

Nonparametric Identification of Finite Mixture Models of Dynamic Discrete Choices

In dynamic discrete choice analysis, controlling for unobserved heterogeneity is an important issue, and finite mixture models provide flexible ways to account for it. This paper studies nonparametric identifiability of type probabilities and type‐specific component distributions in finite mixture models of dynamic discrete choices. We derive sufficient conditions for nonparametric identification for various finite mixture models of dynamic discrete choices used in applied work under different assumptions on the Markov property, stationarity, and type‐invariance in the transition process. Three elements emerge as the important determinants of identification: the time‐dimension of panel data, the number of values the covariates can take, and the heterogeneity of the response of different types to changes in the covariates. For example, in a simple case where the transition function is type‐invariant, a time‐dimension of T = 3 is sufficient for identification, provided that the number of values the covariates can take is no smaller than the number of types and that the changes in the covariates induce sufficiently heterogeneous variations in the choice probabilities across types. Identification is achieved even when state dependence is present if a model is stationary first‐order Markovian and the panel has a moderate time‐dimension (T 6).     

Accuracy of Simulations for Stochastic Dynamic Models

This paper is concerned with accuracy properties of simulations of approximate solutions for stochastic dynamic models. Our analysis rests upon a continuity property of invariant distributions and a generalized law of large numbers. We then show that the statistics generated by any sufficiently good numerical approximation are arbitrarily close to the set of expected values of the model's invariant distributions. Also, under a contractivity condition on the dynamics, we establish error bounds. These results are of further interest for the comparative study of stationary solutions and the estimation of structural dynamic models.     

Estimation of Nonlinear Models with Measurement Error

This paper presents a solution to an important econometric problem, namely the root n consistent estimation of nonlinear models with measurement errors in the explanatory variables, when one repeated observation of each mismeasured regressor is available. While a root n consistent estimator has been derived for polynomial specifications (see Hausman, Ichimura, Newey, and Powell (1991)), such an estimator for general nonlinear specifications has so far not been available. Using the additional information provided by the repeated observation, the suggested estimator separates the measurement error from the “true” value of the regressors thanks to a useful property of the Fourier transform: The Fourier transform converts the integral equations that relate the distribution of the unobserved “true” variables to the observed variables measured with error into algebraic equations. The solution to these equations yields enough information to identify arbitrary moments of the “true,” unobserved variables. The value of these moments can then be used to construct any estimator that can be written in terms of moments, including traditional linear and nonlinear least squares estimators, or general extremum estimators. The proposed estimator is shown to admit a representation in terms of an influence function, thus establishing its root n consistency and asymptotic normality. Monte Carlo evidence and an application to Engel curve estimation illustrate the usefulness of this new approach.     

基于CVaR与矩匹配方法的外汇储备资产配置动态随机优化模型

本文建立了一个动态的随机优化模型,对我国外汇储备在国际资产中的配置问题进行研究。采用基于矩匹配方法生成的情景树代表资产价格波动与币种间汇率变动的不确定性,将管理当局对于外汇储备安全性、流动性、收益性的要求统一纳入模型中,最终计算出外汇储备各资产的动态配置比例。结果表明,运用动态随机优化模型对我国外汇资产进行动态配置灵活、有效。     

Local Identification of Nonparametric and Semiparametric Models

In parametric, nonlinear structural models, a classical sufficient condition for local identification, like Fisher (1966) and Rothenberg (1971), is that the vector of moment conditions is differentiable at the true parameter with full rank derivative matrix. We derive an analogous result for the nonparametric, nonlinear structural models, establishing conditions under which an infinite dimensional analog of the full rank condition is sufficient for local identification. Importantly, we show that additional conditions are often needed in nonlinear, nonparametric models to avoid nonlinearities overwhelming linear effects. We give restrictions on a neighborhood of the true value that are sufficient for local identification. We apply these results to obtain new, primitive identification conditions in several important models, including nonseparable quantile instrumental variable (IV) models and semiparametric consumption‐based asset pricing models.     

Stochastic Algorithms,Symmetric Markov Perfect Equilibrium,and the ‘curse’ of Dimensionality

This paper introduces a stochastic algorithm for computing symmetric Markov perfect equilibria. The algorithm computes equilibrium policy and value functions, and generates a transition kernel for the (stochastic) evolution of the state of the system. It has two features that together imply that it need not be subject to the curse of dimensionality. First, the integral that determines continuation values is never calculated; rather it is approximated by a simple average of returns from past outcomes of the algorithm, an approximation whose computational burden is not tied to the dimension of the state space. Second, iterations of the algorithm update value and policy functions at a single (rather than at all possible) points in the state space. Random draws from a distribution set by the updated policies determine the location of the next iteration's updates. This selection only repeatedly hits the recurrent class of points, a subset whose cardinality is not directly tied to that of the state space. Numerical results for industrial organization problems show that our algorithm can increase speed and decrease memory requirements by several orders of magnitude.     

Identification of Standard Auction Models

This paper presents new identification results for models of first–price, second–price, ascending (English), and descending (Dutch) auctions. We consider a general specification of the latent demand and information structure, nesting both private values and common values models, and allowing correlated types as well as ex ante asymmetry. We address identification of a series of nested models and derive testable restrictions enabling discrimination between models on the basis of observed data. The simplest model—symmetric independent private values—is nonparametrically identified even if only the transaction price from each auction is observed. For richer models, identification and testable restrictions may be obtained when additional information of one or more of the following types is available: (i) the identity of the winning bidder or other bidders; (ii) one or more bids in addition to the transaction price; (iii) exogenous variation in the number of bidders; (iv) bidder–specific covariates. While many private values (PV) models are nonparametrically identified and testable with commonly available data, identification of common values (CV) models requires stringent assumptions. Nonetheless, the PV model can be tested against the CV alternative, even when neither model is identified.     

双寡头电力市场垂直合作减排的随机微分对策模型

考虑单电网公司与双发电商所组成的渠道结构,构建了发电商投资减排、电网公司投资消纳的优势互补的异质型垂直合作减排的随机微分对策模型,先后考察并比较了分散决策和集成决策下的反馈均衡结果。在此基础上,讨论了利润共享契约下系统增量利润的分配问题。研究表明：对于分散决策,电网公司选择性承担发电商的减排费用;两种决策下的发电商减排和电网公司购电价格以及分散决策下的减排补贴与发电商之间的减排竞争强度相关;在一定条件和范围内,合作博弈有利于提高电网公司购电电价,同时为此所带来的风险增大。     

Nonparametric Identification and Estimation of Nonadditive Hedonic Models

This paper studies the identification and estimation of preferences and technologies in equilibrium hedonic models. In it, we identify nonparametric structural relationships with nonadditive heterogeneity. We determine what features of hedonic models can be identified from equilibrium observations in a single market under weak assumptions about the available information. We then consider use of additional information about structural functions and heterogeneity distributions. Separability conditions facilitate identification of consumer marginal utility and firm marginal product functions. We also consider how identification is facilitated using multimarket data.     

基于状态空间的贝叶斯跳跃厚尾金融随机波动模型研究

针对金融市场中跳跃特征的刻画问题,提出了贝叶斯跳跃厚尾随机波动模型。通过随机波动模型的结构分析和状态空间转换,设计了模型参数估计的MCMC算法,利用Kalman滤波和高斯模拟平滑方法估计模型的潜在波动,运用贝叶斯因子对随机波动类模型进行比较分析,并利用中国和美国的股市收益数据进行实证分析。研究结果表明:在刻画中、美两国股票市场的波动特征方面,跳跃厚尾随机波动模型要明显优于厚尾随机波动模型和标准随机波动模型,并且金融危机背景下的中国和美国股票市场都具有明显的波动持续性以及跳跃特征。     

On the Testability of Identification in Some Nonparametric Models With Endogeneity

This paper examines three distinct hypothesis testing problems that arise in the context of identification of some nonparametric models with endogeneity. The first hypothesis testing problem we study concerns testing necessary conditions for identification in some nonparametric models with endogeneity involving mean independence restrictions. These conditions are typically referred to as completeness conditions. The second and third hypothesis testing problems we examine concern testing for identification directly in some nonparametric models with endogeneity involving quantile independence restrictions. For each of these hypothesis testing problems, we provide conditions under which any test will have power no greater than size against any alternative. In this sense, we conclude that no nontrivial tests for these hypothesis testing problems exist.     

具公平行为的零售商回收闭环供应链动态均衡策略研究

本文研究了一主导制造商（再制造商）和一"嫉妒/自豪"公平关切行为的零售商（回收商）组成的闭环供应链系统的成员动态均衡策略。利用伊藤过程，刻画了闭环供应链系统的废旧品回收率的随机演化过程。基于利润结构，给出了制造商的期望利润目标泛函。基于零售商的"嫉妒/自豪"型模式，构建了零售商的期望效用目标泛函，进而建立了闭环供应链系统的随机微分博弈模型。利用随机微分博弈理论，给出了制造商和零售商最优值函数应满足的偏微分方程组。通过求解该偏微分方程组，获得了制造商和零售商最优值函数，据此得到了制造商的动态均衡批发价格策略以及零售商的动态均衡销售价格与回收投入努力策略。为了掌握废旧品回收率的统计特征，揭示了废旧品回收率的随机演进性质。结合算例，研究了零售商公平关切程度对成员均衡策略以及绩效的影响。结果表明：零售商公平关切程度越高，制造商的均衡批发价格越低，进而导致其最优值函数将减少。与制造商不同，零售商公平关切程度将增加其最优值函数。另外，零售商公平关切程度越高，系统回收率越低。     

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.     

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.     

低碳技术组合应用下纵向合作减排的随机微分对策模型

在低碳电力调度准则下,基于考虑减排技术及其协同效应对低碳负荷需求的影响与电网公司购电的有限理性学习过程,建立了发电商采用3种低碳技术组合应用时的纵向合作减排的随机微分对策模型。运用汉密尔顿-雅可比-贝尔曼方程分别求得了Stackelberg博弈和合作博弈下均衡的减排技术投入、稳定的购电电量期望与方差以及Stackelberg博弈下最优的减排支付比例。考察了发电商减排技术的对称性及其应用数量对反馈均衡结果的影响,并对此两种博弈结构进行了比较分析。运用基于双向加权Rubinstein-Stahl讨价还价模型的利润共享契约使得决策系统达到协调,并将模型拓展到多种减排技术投入下的合作减排模型。研究发现:在一定条件和范围内,减排投入提高电网公司购电电量,同时发电商为此所带来的风险增大;合作博弈更适于优化电力市场电源结构,顺应低碳电力调度政策的导向;最优的减排技术应用、稳定的购电电量期望与方差以及系统均衡利润都与发电商选择的低碳技术投资效率、技术之间的协同作用以及数量正相关。     

典型事实约束下的上海燃油期货市场动态VaR测度研究

期货交易的高杠杆率意味着期货市场的高风险特征,而能源市场因其特殊的战略意义一直以来备受关注,因而对能源期货市场的风险测度对投资者和监管者都极其重要。本文对上海燃油期货构建了四个反映不同交割期限的连续价格序列,基于不同的金融市场典型事实分别运用GARCH、GJR、FIGARCH三个模型对波动率建模,并假设条件收益分别服从正态、学生t、有偏学生t(skst)分布进行动态风险价值(VaR)测度,然后运用严格的似然比(LR)检验和动态分位数回归(DQR)检验对风险测度的可靠性进行后验分析(Backtesting),尝试从中提取出在风险管理中最有应用价值的典型事实。研究发现:(1)基于skst分布的波动模型的动态风险测度准确性明显优于其他分布下的相同模型;(2)基于杠杆效应的GJR模型和基于长记忆性的FIGARCH模型并没有表现出比普通GARCH模型更高的精度;(3)远期合约的市场平均收益更高,风险测度比近期合约更准确。     

Solving,Estimating, and Selecting Nonlinear Dynamic Models Without the Curse of Dimensionality

We present a comprehensive framework for Bayesian estimation of structural nonlinear dynamic economic models on sparse grids to overcome the curse of dimensionality for approximations. We apply sparse grids to a global polynomial approximation of the model solution, to the quadrature of integrals arising as rational expectations, and to three new nonlinear state space filters which speed up the sequential importance resampling particle filter. The posterior of the structural parameters is estimated by a new Metropolis–Hastings algorithm with mixing parallel sequences. The parallel extension improves the global maximization property of the algorithm, simplifies the parameterization for an appropriate acceptance ratio, and allows a simple implementation of the estimation on parallel computers. Finally, we provide all algorithms in the open source software JBendge for the solution and estimation of a general class of models.     

多元化集成模式下交通基础设施建设融资风险识别研究

从单一模式向多元化集成模式转变已成为新形势下拓宽交通基础设施建设融资渠道的必然选择,在规避单一模式局限、形成多元模式优势合力的同时,也加剧了融资不确定性和复杂性,从而对融资风险识别提出了更高的要求。综合考虑多元化集成模式下交通基础设施建设融资风险的多源性、关联性、模糊性、随机性等典型表征,设计了与典型表征相匹配的融资风险识别框架,并构建了融资风险识别两阶段模型。在该模型中,明确了融资风险因素确定流程和维度划分规则,并提出了基于随机二元语义DEMATEL的风险因素关联分析方法进行融资风险因素的研判与诊断。最后,以“PPP+ABS”模式下大成西黄河大桥收益权计划为例开展了计算实验,验证了所构建模型的有效性,并给出了融资风险应对与化解的启示与建议。研究成果能够为相关利益主体明晰融资风险因素构成、研判融资风险因素影响力、诊断融资风险因素可控性、有效应对与化解融资风险提供系统性解决方案。