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结构化高维模型中泛函的极小极大估计AI 解读

We introduce a new method of estimation of parameters in semiparametric and nonparametric models. The method employs $U$-statistics that are based on higher-order influence functions of the parameter of interest, which extend ordinary linear influence functions, and represent higher derivatives of this parameter. For parameters for which the representation cannot be perfect the method often leads to a bias-variance trade-off, and results in estimators that converge at a…

非均衡信念下第一价格拍卖的识别：测量误差方法AI 解读

This paper studies identification and estimation of two models for first-price auctions: (1) bidders’ beliefs about their opponents’ bidding behavior are not in equilibrium but follow “level-k” thinking, and (2) bidders’ values are asymmetrically distributed. Exploiting the nonparametric methodology developed for measurement error models (e.g., Hu, 2008), we show that both models can be identified by a unified methodology. The proposed methodology is applied to US Forest…

无工具变量下误测解释变量的可加与多项式模型识别AI 解读

We show nonparametric point identification of a measurement error model with covariates that can be interpreted as invalid instruments. Our main contribution is to replace standard exclusion restrictions with the weaker assumption of additivity in the covariates. Measurement errors are ubiquitous and additive models are popular, so our results combining the two should have widespread potential application. We also identify a model that replaces the nonparametric function of…

受限矩模型中未知异方差误差分布与协变量误测的联合处理AI 解读

We develop consistent and efficient estimation of parameters in general regression models with mismeasured covariates. We assume the model error and covariate distributions are unspecified, and the measurement error distribution is a general parametric distribution with unknown variance–covariance. We construct root-n consistent, asymptotically normal and locally efficient estimators using the semiparametric efficient score. We do not estimate any unknown distribution or…

利用控制变量估计含非经典测量误差的非线性模型AI 解读

We consider nonlinear parametric models with an independent variable that is measured with error. The measurement error can be correlated with the true value, i.e., the measurement error is allowed to be nonclassical. We propose a control variable estimator for the parameters of interest. The estimator is consistent even if the latent true value is endogenous. We derive the influence function of the semi-parametric estimator that accounts for the estimation of the control…

扩展Roll模型中买卖价差的半参数识别AI 解读

This paper provides new identification results for the bid–ask spread and the nonparametric distribution of the latent fundamental price increments (εt) from the observed transaction prices alone. The results are established via the characteristic function approach, and hence allow for discrete or continuous εt and the observed price increments do not need to have any finite moments. Constructive identification (and overidentification) results are established first in the…

运行变量存在连续测量误差的回归不连续设计AI 解读

Since the late 90s, Regression Discontinuity (RD) designs have been widely used to estimate Local Average Treatment Effects (LATE). When the running variable is observed with continuous measurement error, identification fails. Assuming non-differential measurement error, we propose a consistent nonparametric estimator of the LATE when the discrepancy between the true running variable and its noisy measure is observed in an auxiliary sample of treated individuals, and when…

不可观测变量计量：测量误差模型在产业组织与劳动经济学中的应用AI 解读

This paper reviews the recent developments in nonparametric identification of measurement error models and their applications in applied microeconomics, in particular, in empirical industrial organization and labor economics. Measurement error models describe mappings from a latent distribution to an observed distribution. The identification and estimation of measurement error models focus on how to obtain the latent distribution and the measurement error distribution from…

α 混合三角阵列和的收敛速率及连续时间漂移函数估计应用AI 解读

The convergence rates of the sums of α -mixing (or strongly mixing) triangular arrays of heterogeneous random variables are derived. We pay particular attention to the case where central limit theorems may fail to hold, due to relatively strong time-series dependence and/or the nonexistence of higher-order moments. Several previous studies have presented various versions of laws of large numbers for sequences/triangular arrays, but their convergence rates were not fully…

区间删失变量下加权平均导数的渐近有效估计AI 解读

This paper studies the identification and estimation of weighted average derivatives of conditional location functionals including conditional mean and conditional quantiles in settings where either the outcome variable or a regressor is interval-valued. Building on Manski and Tamer (2002, Econometrica 70(2), 519–546) who study nonparametric bounds for mean regression with interval data, we characterize the identified set of weighted average derivatives of regression…

互补性与识别AI 解读

This paper examines the identification power of assumptions that formalize the notion of complementarity in the context of a nonparametric bounds analysis of treatment response. I extend the literature on partial identification via shape restrictions by exploiting cross-dimensional restrictions on treatment response when treatments are multidimensional; the assumption of supermodularity can strengthen bounds on average treatment effects in studies of policy complementarity.…

半参数条件矩限制模型的广义经验似然估计札记AI 解读

This paper proposes an empirical likelihood-based estimation method for semiparametric conditional moment restriction models, which contain finite dimensional unknown parameters and unknown functions. We extend the results of Donald, Imbens, and Newey (2003, Journal of Econometrics 117, 55–93) by allowing unknown functions to be included in the conditional moment restrictions. We approximate unknown functions by a sieve method and estimate the finite dimensional parameters…

局部分块分位数回归AI 解读

In this paper, we consider the nonparametric estimation of a broad class of quantile regression models, in which the partially linear, additive, and varying coefficient models are nested. We propose for the model a two-stage kernel-weighted least squares estimator by generalizing the idea of local partitioned mean regression (Christopeit and Hoderlein, 2006, Econometrica 74, 787–817) to a quantile regression framework. The proposed estimator is shown to have desirable…

具有尖峰—连续混合基测度的 Pitman–Yor 过程AI 解读

Summary For the most popular discrete nonparametric models, beyond the Dirichlet process, the prior guess at the shape of the data-generating distribution, also known as the base measure, is assumed to be diffuse. Such a specification greatly simplifies the derivation of analytical results, allowing for a straightforward implementation of Bayesian nonparametric inferential procedures. However, in several applied problems the available prior information leads naturally to the…

多元区间删失数据半参数回归模型的最大似然估计AI 解读

Interval-censored multivariate failure time data arise when there are multiple types of failure or there is clustering of study subjects and each failure time is known only to lie in a certain interval. We investigate the effects of possibly time-dependent covariates on multivariate failure times by considering a broad class of semiparametric transformation models with random effects, and we study nonparametric maximum likelihood estimation under general interval-censoring…

利用无偏估计量平均值的伪边际 Metropolis–Hastings 抽样AI 解读

Summary We consider a pseudo-marginal Metropolis–Hastings kernel ${\mathbb{P}}_m$ that is constructed using an average of $m$ exchangeable random variables, and an analogous kernel ${\mathbb{P}}_s$ that averages $s<m$ of these same random variables. Using an embedding technique to facilitate comparisons, we provide a lower bound for the asymptotic variance of any ergodic average associated with ${\mathbb{P}}_m$ in terms of the asymptotic variance of the corresponding ergodic…

因子化似然模型中的多重稳健性AI 解读

We consider inference under a nonparametric or semiparametric model with likelihood that factorizes as the product of two or more variation-independent factors. We are interested in a finite-dimensional parameter that depends on only one of the likelihood factors and whose estimation requires the auxiliary estimation of one or several nuisance functions. We investigate general structures conducive to the construction of so-called multiply robust estimating functions, whose…

复杂生存数据的 non-strange weird 重抽样AI 解读

This paper introduces a new data-dependent multiplier bootstrap for nonparametric analysis of survival data, possibly subject to competing risks. The nw procedure includes the general wild bootstrap and the weird bootstrap as special cases. The data may be subject to independent right-censoring and left-truncation. The asymptotic correctness of the proposed resampling procedure is proven under standard assumptions. Simulation results on time-simultaneous inference suggest…

联合充分降维与条件及平均处理效应估计AI 解读

The estimation of treatment effects based on observational data usually involves multiple confounders, and dimension reduction is often desirable and sometimes inevitable. We first clarify the definition of a central subspace that is relevant for the efficient estimation of average treatment effects. A criterion is then proposed to simultaneously estimate the structural dimension, the basis matrix of the joint central subspace, and the optimal bandwidth for estimating the…

随机缺失数据下的条件矩模型AI 解读

We consider a general statistical model defined by moment restrictions when data are missing at random. Using inverse probability weighting, we show that such a model is equivalent to a model for the observed variables only, augmented by a moment condition defined by the missingness mechanism. Our framework covers parametric and semiparametric mean regressions and quantile regressions. We allow for missing responses, missing covariates and any combination of them. The…

连续处理效应双重稳健估计的非参数方法AI 解读

Summary Continuous treatments (e.g. doses) arise often in practice, but many available causal effect estimators are limited by either requiring parametric models for the effect curve, or by not allowing doubly robust covariate adjustment. We develop a novel kernel smoothing approach that requires only mild smoothness assumptions on the effect curve and still allows for misspecification of either the treatment density or outcome regression. We derive asymptotic properties and…

最大化 t 年生存概率的最优治疗策略估计AI 解读

Summary A treatment regime is a deterministic function that dictates personalized treatment based on patients’ individual prognostic information. There is increasing interest in finding optimal treatment regimes, which determine treatment at one or more treatment decision points to maximize expected long-term clinical outcomes, where larger outcomes are preferred. For chronic diseases such as cancer or human immunodeficiency virus infection, survival time is often the…

基于最小相对熵的响应与协变量关联模型AI 解读

Summary A semiparametric model is presented utilizing dependence between a response and several covariates. We show that this model is optimum when the marginal distributions of the response and the covariates are known. This model extends the generalized linear model and the proportional likelihood ratio model when the marginal distributions are unknown. New interpretations of known models such as the logistic regression model, density ratio model and selection bias model…

非因果向量自回归过程：表示、识别与半参数估计AI 解读

This paper introduces a representation theorem for a mixed VAR(p) process by distinguishing its causal and noncausal components. That representation is used to discuss the advantages and limitations of second-order identification in a mixed VAR. We show that it is possible to find the numbers of causal or noncausal components of the process from its multivariate autocovariance function, while nonlinear autocovariances are needed to distinguish between them. The paper…