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复合分位数回归与模型选择的预言机理论AI 解读

Coefficient estimation and variable selection in multiple linear regression is routinely done in the (penalized) least squares (LS) framework. The concept of model selection oracle introduced by Fan and Li [J. Amer. Statist. Assoc. 96 (2001) 1348–1360] characterizes the optimal behavior of a model selection procedure. However, the least-squares oracle theory breaks down if the error variance is infinite. In the current paper we propose a new regression method called…

资格随机设计中的处理反应数据分析AI 解读

In this paper, we develop and compare two alternative approaches for calculating the effect of the actual intake when treatments are randomized, but compliance with the assignment in the treatment arm is less than perfect for reasons that are correlated with the outcome. The approaches are based on different identification assumptions about these unobserved confounders. In the first approach, which stems from [Sommer, A., Zeger, S., 1991. On estimating efficacy in clinical…

协变量门限引致变点的二元响应模型半参数估计AI 解读

This paper is concerned with semiparametric estimation of a threshold binary response model. The estimation method considered in the paper is semiparametric since the parameters for a regression function are finite-dimensional, while allowing for heteroskedasticity of unknown form. In particular, the paper considers Manski's [Manski, Charles F., 1975. Maximum score estimation of the stochastic utility model of choice. Journal of Econometrics 3 (3), 205-228; Manski, Charles…

不可观测因素引致样本流失时的面板模型推断AI 解读

This paper concerns identification and estimation of a finite-dimensional parameter in a panel data-model under nonignorable sample attrition. Attrition can depend on second period variables which are unobserved for the attritors but an independent refreshment sample from the marginal distribution of the second period values is available. This paper shows that under a quasi-separability assumption, the model implies a set of conditional moment restrictions where the moments…

大学专业与收入：利用关联程度识别非可忽略无应答偏差AI 解读

We link information on graduates from many cohorts to their high-school and college records and demographics to infer the impact of college major on earnings. We develop an estimator to handle potential non-response bias and identify non-response using an affinity measure--the potential respondent's link to the survey organization. This technique is generally applicable for adjusting for unit non-response. In the earnings model estimated using the identified (for…

双重差分与广义最小二乘：假设检验的高阶性质AI 解读

We investigate the estimation and inference in difference in difference econometric models used in the analysis of treatment effects. When the innovations in such models display serial correlation, commonly used ordinary least squares (OLS) procedures are inefficient and may lead to tests with incorrect size. Implementation of feasible generalized least squares (FGLS) procedures is often hindered by too few observations in the cross-section to allow for unrestricted…

协方差平稳面板 AR(1)／单位根模型的最大似然估计与推断AI 解读

This paper considers Maximum Likelihood (ML) based estimation and inference procedures for linear dynamic panel data models with fixed effects. The paper first studies the asymptotic properties of MaCurdy's [MaCurdy, T., 1982. The use of time series processes to model the time structure of earnings in a longitudinal data analysis. Journal of Econometrics 18, 83-114] First Difference Maximum Likelihood (FDML) estimator for the covariance stationary panel AR(1)/unit rootÂ…

医疗试验中的患者入组：随机实验的选择偏差AI 解读

Self-selection can bias estimates of treatment effects from randomized experiments if one is interested in extrapolating results to the general population. This paper notes that there is an isomorphism between the Roy model for the sorting of workers into sectors and the decision of subjects to participate in randomized experiments. The main implication is that, as the probability of receiving active treatment rises, patients who are less optimistic about new treatment will…

低技能移民对美国物价的影响：来自CPI数据的证据AI 解读

I exploit the large variation across U.S. cities and through time in the relative size of the low-skilled immigrant population to estimate the causal effect of immigration on prices of nontraded goods and services. Using an instrumental variables strategy, I find that, at current immigration levels, a 10 percent increase in the share of low-skilled immigrants in the labor force decreases the price of immigrant-intensive services, such as housekeeping and gardening, by 2…

单调指数模型的排序估计量AI 解读

This paper shows that the finite-dimensional parameters of a monotone-index model can be estimated by minimizing an objective function based on sorting the data. The key observation guiding this procedure is that the sum of distances between pairs of adjacent observations is minimized (over all possible permutations) when the observations are sorted by their values. The resulting estimator is a generalization of Cavanagh and Sherman's monotone rank estimator (MRE) (Cavanagh…

半非参数区间删失混合比例风险模型：识别与一致性结果AI 解读

In this paper I propose estimating distributions on the unit interval semi-nonparametrically using orthonormal Legendre polynomials. This approach will be applied to the interval-censored mixed proportional hazard (ICMPH) model, where the distribution of the unobserved heterogeneity is modeled semi-nonparametrically. Various conditions for the nonparametric identification of the ICMPH model are derived. I will prove general consistency results for M -estimators of (partly)…

有无单位根的 AR(1) 时间序列中的高斯推断AI 解读

This paper introduces a simple first-difference-based approach to estimation and inference for the AR(1) model. The estimates have virtually no finite-sample bias and are not sensitive to initial conditions, and the approach has the unusual advantage that a Gaussian central limit theory applies and is continuous as the autoregressive coefficient passes through unity with a uniform $\sqrt{n}$ rate of convergence. En route, a useful central limit theorem (CLT) for sample…

利用模拟得分估计与效用一致的劳动供给模型AI 解读

Abstract We consider a utility‐consistent static labor supply model with flexible preferences and a nonlinear and possibly non‐convex budget set. Stochastic error terms are introduced to represent optimization and reporting errors, stochastic preferences, and heterogeneity in wages. Coherency conditions on the parameters and the support of error distributions are imposed for all observations. The complexity of the model makes it impossible to write down the probability of…

墨西哥裔美国人平均工资差距的非参数分解AI 解读

Abstract This paper shows that average wage gap decompositions between any two groups of workers can be carried out using nonparametric wage structures. It also proposes an algorithm to correct for sample selection in nonparametric models known as tree structures. This paper studies the wage gap between third‐generation Mexican American and non‐Hispanic white workers in the southwest. It is shown that the decomposition heavily depends on functional assumptions, and that…

误分类数据下概率分布的部分识别AI 解读

This paper addresses the problem of data errors in discrete variables. When data errors occur, the observed variable is a misclassified version of the variable of interest, whose distribution is not identified. Inferential problems caused by data errors have been conceptualized through convolution and mixture models. This paper introduces the direct misclassification approach. The approach is based on the observation that in the presence of classification errors, the…

工具变量问题的半参数贝叶斯方法AI 解读

We develop a Bayesian semi-parametric approach to the instrumental variable problem. We assume linear structural and reduced form equations, but model the error distributions non-parametrically. A Dirichlet process prior is used for the joint distribution of structural and instrumental variable equations errors. Our implementation of the Dirichlet process prior uses a normal distribution as a base model. It can therefore be interpreted as modeling the unknown joint…

固定效应面板模型的非参数估计与检验AI 解读

In this paper we consider the problem of estimating nonparametric panel data models with fixed effects. We introduce an iterative nonparametric kernel estimator. We also extend the estimation method to the case of a semiparametric partially linear fixed effects model. To determine whether a parametric, semiparametric or nonparametric model is appropriate, we propose test statistics to test between the three alternatives in practice. We further propose a test statistic for…

非参数联立方程模型的局部多项式估计AI 解读

We define a new procedure for consistent estimation of nonparametric simultaneous equations models under the conditional mean independence restriction of Newey et al. [1999. Nonparametric estimation of triangular simultaneous equation models. Econometrica 67, 565-603]. It is based upon local polynomial regression and marginal integration techniques. We establish the asymptotic distribution of our estimator under weak data dependence conditions. Simulation evidence suggests…

工具变量分位数模型中的弱识别稳健检验AI 解读

We develop a testing procedure that is robust to identification quality in an instrumental quantile model. In order to reduce the computational burden, a multi-step approach is taken, and a two-step Anderson-Rubin (AR) statistic is considered. We then propose an orthogonal decomposition of the AR statistic, where the null distribution of each component does not depend on the assumption of a full rank of the Jacobian. Power experiments are conducted, and inferences on returns…

利用稀疏网格数值积分近似似然AI 解读

The calculation of likelihood functions of many econometric models requires the evaluation of integrals without analytical solutions. Approaches for extending Gaussian quadrature to multiple dimensions discussed in the literature are either very specific or suffer from exponentially rising computational costs in the number of dimensions. We propose an extension that is very general and easily implemented, and does not suffer from the curse of dimensionality. Monte Carlo…

利用工具变量识别和估计含误分类误差的非线性模型：一般解法AI 解读

This paper provides a general solution to the problem of identification and estimation of nonlinear models with misclassification error in a general discrete explanatory variable using instrumental variables. The misclassification error is allowed to be correlated with all the explanatory variables in the model. It is not enough to identify the model by simply generalizing the identification in the binary case with a claim that the number of restrictions is no less than that…

分析师覆盖与盈余管理AI 解读

What is the role of information intermediaries in corporate governance? This paper examines equity analysts' influence on managers' earnings management decisions. Do analysts serve as external monitors to managers, or do they put excessive pressure on managers? Using multiple measures of earnings management, I find that firms followed by more analysts manage their earnings less. To address the potential endogeneity problem of analyst coverage, I use two instrumental…

通过条件方差限制识别社会互动AI 解读

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中位数约束模型中基于符号与秩的半参数有效推断AI 解读

Summary Since the pioneering work of Koenker and Bassett, median-restricted models have attracted considerable interest. Attention in these models, so far, has focused on least absolute deviation (auto-)regression quantile estimation and the corresponding sign tests. These methods use a pseudolikelihood that is based on a double-exponential reference density and enjoy quite attractive properties of root n consistency (for estimators) and distribution freeness (for tests).…