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Testing for Causal Effects in a Generalized Regression Model With Endogenous Regressors

含内生解释变量的广义回归模型中的因果效应检验

A unifying framework to test for causal effects in nonlinear models is proposed. We consider a generalized linear-index regression model with endogenous regressors and no parametric assumptions on the error disturbances. To test the significance of the effect of an endogenous regressor, we propose a statistic that is a kernel-weighted version of the rank correlation statistic (tau) of Kendall (1938). The semiparametric model encompasses previous cases considered in the…

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Alternative Models for Moment Inequalities

矩不等式的替代模型

Behavioral choice models generate inequalities which, when combined with additional assumptions, can be used as a basis for estimation. This paper considers two sets of such assumptions and uses them in two empirical examples. The second example examines the structure of payments resulting from the upstream interactions in a vertical market. I then mimic the empirical setting for this example in a numerical analysis which computes actual equilibria, examines how their…

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Irregular Identification, Support Conditions, and Inverse Weight Estimation

非正则识别、支持条件与逆权重估计

In weighted moment condition models, we show a subtle link between identification and estimability that limits the practical usefulness of estimators based on these models. In particular, if it is necessary for (point) identification that the weights take arbitrarily large values, then the parameter of interest, though point identified, cannot be estimated at the regular (parametric) rate and is said to be irregularly identified. This rate depends on relative tail conditions…

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Asymptotic Equivalence of Probabilistic Serial and Random Priority Mechanisms

概率序列机制与随机优先机制的渐近等价性

The random priority (random serial dictatorship) mechanism is a common method for assigning objects. The mechanism is easy to implement and strategy-proof. However, this mechanism is inefficient, because all agents may be made better off by another mechanism that increases their chances of obtaining more preferred objects. This form of inefficiency is eliminated by a mechanism called probabilistic serial, but this mechanism is not strategy-proof. Thus, which mechanism to…

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Does Experience Teach? Professionals and Minimax Play in the Lab

经验有助于学习吗?专业人士与实验室中的极小极大策略

Does expertise in strategic behavior obtained in the field transfer to the abstract setting of the laboratory? Palacios-Huerta and Volij (2008) argued that the behavior of professional soccer players in mixed-strategy games conforms closely to minimax play, while the behavior of students (who are presumably novices in strategic situations requiring unpredictability) does not. We reexamine their data, showing that the play of professionals is inconsistent with the minimax…

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Quantile and Probability Curves Without Crossing

无交叉的分位数与概率曲线

This paper proposes a method to address the longstanding problem of lack of monotonicity in estimation of conditional and structural quantile functions, also known as the quantile crossing problem (Bassett and Koenker (1982)). The method consists in sorting or monotone rearranging the original estimated non-monotone curve into a monotone rearranged curve. We show that the rearranged curve is closer to the true quantile curve than the original curve in finite samples,…

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Estimating the Technology of Cognitive and Noncognitive Skill Formation

认知与非认知技能形成技术的估计

This paper formulates and estimates multistage production functions for child cognitive and noncognitive skills. Output is determined by parental environments and investments at different stages of childhood. We estimate the elasticity of substitution between investments in one period and stocks of skills in that period to assess the benefits of early investment in children compared to later remediation. We establish nonparametric identification of a general class of…

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Bootstrap Inference in Partially Identified Models Defined by Moment Inequalities: Coverage of the Identified Set

矩不等式部分识别模型的 Bootstrap 推断:识别集覆盖

This paper introduces a novel bootstrap procedure to perform inference in a wide class of partially identified econometric models. We consider econometric models defined by finitely many weak moment inequalities,2 which encompass many applications of economic interest. The objective of our inferential procedure is to cover the identified set with a prespecified probability.3 We compare our bootstrap procedure, a competing asymptotic approximation, and subsampling procedures…

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Sequential Estimation of Dynamic Discrete Games: A Comment

动态离散博弈的序贯估计:评论

Recursive procedures which are based on iterating on the best response mapping have difficulties converging to all equilibria in multi-player games. We illustrate these difficulties by revisiting the asymptotic properties of the iterative nested pseudo maximum likelihood method for estimating dynamic games introduced by Aguirregabiria and Mira (2007). An example shows that the iterative method may not be consistent. Copyright 2010 The Econometric Society.

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Constructing Optimal Instruments by First-Stage Prediction Averaging

通过第一阶段预测平均构造最优工具变量

This paper considers model averaging as a way to construct optimal instruments for the two-stage least squares (2SLS), limited information maximum likelihood (LIML), and Fuller estimators in the presence of many instruments. We propose averaging across least squares predictions of the endogenous variables obtained from many different choices of instruments and then use the average predicted value of the endogenous variables in the estimation stage. The weights for averaging…

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

避免维度诅咒的非线性动态模型求解、估计与选择

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…

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Inference for the Identified Set in Partially Identified Econometric Models

部分识别计量模型中识别集的统计推断

This paper provides computationally intensive, yet feasible methods for inference in a very general class of partially identified econometric models. Let P denote the distribution of the observed data. The class of models we consider is defined by a population objective function Q(θ, P) for θ∈Θ. The point of departure from the classical extremum estimation framework is that it is not assumed that Q(θ, P) has a unique minimizer in the parameter space Θ. The goal may be either…

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Inference for Parameters Defined by Moment Inequalities Using Generalized Moment Selection

利用广义矩选择对矩不等式定义的参数进行推断

The topic of this paper is inference in models in which parameters are defined by moment inequalities and/or equalities. The parameters may or may not be identified. This paper introduces a new class of confidence sets and tests based on generalized moment selection (GMS). GMS procedures are shown to have correct asymptotic size in a uniform sense and are shown not to be asymptotically conservative. The power of GMS tests is compared to that of subsampling, m out of n…

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Partial Identification of the Distribution of Treatment Effects in Switching Regime Models and its Confidence Sets

切换机制模型中处理效应分布的部分识别及其置信集

In this paper, we establish sharp bounds on the joint distribution of potential outcomes and the distribution of treatment effects in parametric switching regime models with normal mean-variance mixture errors and in the semi-parametric switching regime models of Heckman (1990). Our results for parametric switching regime models with normal mean-variance mixture errors extend some existing results for the Gaussian switching regime model and our results for semi-parametric…

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Tests and confidence intervals for secondary endpoints in sequential clinical trials

序贯临床试验中次要终点的检验与置信区间

In a sequential clinical trial whose stopping rule depends on the primary endpoint, inference on secondary endpoints is an important long-standing problem. Ignoring the possibility of early stopping based on the primary endpoint may result in substantial bias. To address this problem, a commonly used approach is to develop bias correction by estimating the bias in the case of bivariate normal outcomes and appealing to joint asymptotic normality of the statistics associated…

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Marginal hazards model for case-cohort studies with multiple disease outcomes

多疾病结局病例队列研究的边际风险率模型

Case-cohort study designs are widely used to reduce the cost of large cohort studies while achieving the same goals, especially when the disease rate is low. A key advantage of the case-cohort study design is its capacity to use the same subcohort for several diseases or for several subtypes of disease. In order to compare the effect of a risk factor on different types of diseases, times to different events need to be modelled simultaneously. Valid statistical methods that…

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Inference on population size in binomial detectability models

二项可探测性模型中的种群规模推断

Many models for biological populations, including simple mark-recapture models and distance sampling models, involve a binomially distributed number, n, of observations x1, …, xn on members of a population of size N. Two popular estimators of (N, θ), where θ is a vector parameter, are the maximum likelihood estimator and the conditional maximum likelihood estimator based on the conditional distribution of x1, …, xn given n. We derive the large-N asymptotic distributions of…

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Bias reduction in exponential family nonlinear models

指数族非线性模型中的偏差缩减

In Firth (1993, Biometrika) it was shown how the leading term in the asymptotic bias of the maximum likelihood estimator is removed by adjusting the score vector, and that in canonical-link generalized linear models the method is equivalent to maximizing a penalized likelihood that is easily implemented via iterative adjustment of the data. Here a more general family of bias-reducing adjustments is developed for a broad class of univariate and multivariate generalized…

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Sinh-arcsinh distributions

双曲正弦—反双曲正弦分布族

We introduce the sinh-arcsinh transformation and hence, by applying it to a generating distribution with no parameters other than location and scale, usually the normal, a new family of sinh-arcsinh distributions. This four-parameter family has symmetric and skewed members and allows for tailweights that are both heavier and lighter than those of the generating distribution. The central place of the normal distribution in this family affords likelihood ratio tests of…

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Generalized fiducial inference for wavelet regression

小波回归的广义 fiducial 推断

We apply Fisher’s fiducial idea to wavelet regression, first developing a general methodology for handling model selection problems within the fiducial framework. We propose fiducial-based methods for wavelet curve estimation and the construction of pointwise confidence intervals. We show that these confidence intervals have asymptotically correct coverage. Simulations demonstrate that they possess promising empirical properties.

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A note on the variance of doubly-robust G-estimators

双重稳健 G 估计量方差的一则说明

A recursive variance calculation is derived for doubly-robust G-estimators for dynamic treatment regimes in a multi-interval setting. Treatment decision parameters are not assumed to be shared across treatment intervals; this independence of parameters permits sequential estimation of the G-estimators’ variance when G-estimation is performed in a sequential fashion. The recursive variance calculation is both natural and computationally feasible. This development can easily…

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Nonparametric estimation of the probability of illness in the illness-death model under cross-sectional sampling

横截面抽样下疾病—死亡模型患病概率的非参数估计

Cross-sectional sampling is an attractive design that saves resources but results in biased data. For proper inference, one should first discover the bias function and then weigh observations appropriately. We consider cross-sectioning of the illness-death model with the aim of estimating the probability of visiting the illness state before death. We develop simple consistent and asymptotically normal estimators under various assumptions on the model and data collection and,…

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Bayesian lasso regression

贝叶斯 lasso 回归

The lasso estimate for linear regression corresponds to a posterior mode when independent, double-exponential prior distributions are placed on the regression coefficients. This paper introduces new aspects of the broader Bayesian treatment of lasso regression. A direct characterization of the regression coefficients’ posterior distribution is provided, and computation and inference under this characterization is shown to be straightforward. Emphasis is placed on point…

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Local quasi-likelihood with a parametric guide

带参数引导的局部拟似然

Generalized linear models and quasi-likelihood method extend the ordinary regression models to accommodate more general conditional distributions of the response. Nonparametric methods need no explicit parametric specification and the resulting model is completely determined by the data themselves. However nonparametric estimation schemes generally have a slower convergence rate such as the local polynomial smoothing estimation of nonparametric generalized linear models…

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