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Hodges-Lehmann Point Estimates of Treatment Effect in Observational Studies

观察性研究中处理效应的Hodges–Lehmann点估计

A Hodges-Lehmann point estimate of an additive treatment effect is a robust estimate derived from the randomization distribution of a rank test. This article shows how to carry out a sensitivity analysis for such an estimate in an observational study where treatments are not randomly assigned. Two cases are discussed in detail: matched pairs and the comparison of two unmatched groups. The method uses a model for the distribution of treatment assignments when hidden biases…

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Hodges-Lehmann Point Estimates of Treatment Effect in Observational Studies

观察研究中治疗效应的 Hodges–Lehmann 点估计

A Hodges-Lehmann point estimate of an additive treatment effect is a robust estimate derived from the randomization distribution of a rank test. This article shows how to carry out a sensitivity analysis for such an estimate in an observational study where treatments are not randomly assigned. Two cases are discussed in detail: matched pairs and the comparison of two unmatched groups. The method uses a model for the distribution of treatment assignments when hidden biases…

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Consumer Purchase Behavior in a Frequently Bought Product Category: Estimation Issues and Managerial Insights from a Hazard Function Model with Heterogeneity

高频购买品类的消费者行为:异质性风险函数模型的估计与管理启示

Abstract We apply hazard function models to marketing data on household purchases of disposable diapers to calibrate repeat-purchase and brand-switching behaviors. In the model we allow for household-specific unobserved traits. Hazard function models with fixed-effects heterogeneity specification are computationally feasible. We directly compare the results of fixed- and random-effects heterogeneity specifications in the context of parametric and nonparametric…

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Likelihood Analysis for Probit Regression with Measurement Errors

含测量误差的 Probit 回归似然分析

Likelihood analysis is proposed for a probit regression model when one of the explanatory variables is measured with error and replicate measurements of that variable are available on some of the subjects. The distribution of the measurement error and of the unknown explanatory variable, conditional on the known variables, are both taken to be normal. The maximum likelihood estimates can be computed exactly with the EM algorithm. Likelihood ratio tests and confidence…

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Using Surrogate Failure Time Data to Increase Cost Effectiveness in Clinical Trials

利用替代失效时间数据提高临床试验的成本效益

In clinical trials with failure time, outcomes, the cost of follow-up for the endpoint of primary interest is sometimes expensive. In many of these settings, one or more secondary or surrogate failure time outcomes are also available which either occur much earlier or are much less expensive to follow than the primary outcome. This paper presents a method for using these secondary outcomes to increase the power of detecting a treatment effect on the primary outcome only,…

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A Geometric Approach to Assess Bias Due to Omitted Covariates in Generalized Linear Models

用几何方法评估广义线性模型中的遗漏协变量偏差

Misspecifying generalized linear models by omitting covariates associated with the response can result in seriously biased estimates of the effects of the included covariates. For certain models, such bias can occur even when the omitted covariate is independent of the included covariates, as in studies with randomized treatment assignment. This paper presents a geometric approach to assess the direction of the bias resulting from omitted covariates and develop intuition…

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Statistical Inference in the Measurement of Poverty

贫困测度中的统计推断

Several poverty indices have been suggested to measure the intensity of poverty suffered by those below the poverty line. Because the indices are estimated on the basis of sample observations, we need to test whether the observed differences in their values are statistically significant. This paper provides distribution-free asymptotic confidence intervals and statistical inference for additive poverty indices. The methodology developed in the paper is applied to analyze…

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Engel Functions, Panel Data, and Latent Variables

恩格尔函数、面板数据与潜变量

Systems of consumer expenditure functions are estimated from Norwegian household panel data. Total consumption expenditure is modeled as a latent variable, as indicators of it are purchase expenditures on different goods and two income measures. The usual assumption of no measurement error in total expenditure is clearly rejected while a parsimonious alternative is not. The authors test hypotheses regarding the distribution of measurement errors, the evolution of the…

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Shadow Wages and Peasant Family Labour Supply: An Econometric Application to the Peruvian Sierra

影子工资与农户家庭劳动供给:秘鲁山区的计量应用

This paper develops a methodology for estimating structural time-allocation models for self-employed households and applies it to peasant family labour supply behaviour in the Peruvian Sierra. The opportunity costs of time, or shadow wages, of household workers are explicitly estimated from an agricultural production function. Using an instrumental variables procedure, the household's structural labour supply parameters are recovered from variation in these shadow wages. The…

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How does the benefit effect vary as unemployment spells lengthen?

失业持续期延长时,失业收入的影响如何变化?

Abstract This paper investigates how the effect of income while unemployed on the probability of an individual leaving unemployment varies with the length of time that the individual has been unemployed. We examine this question in the context of a variety of alternative econometric models. We extend the Proportional Hazards model with unrestricted baseline hazard to one in which there are unrestricted effects of a subset of the explanatory variables and also consider models…

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Smoothed Empirical Likelihood Confidence Intervals for Quantiles

分位数的平滑经验似然置信区间

Standard empirical likelihood confidence intervals for quantiles are identical to sign-test intervals. They have relatively large coverage error, of size $n^{-1/2}$, even though they are two-sided intervals. We show that smoothed empirical likelihood confidence intervals for quantiles have coverage error of order $n^{-1}$, and may be Bartlett-corrected to produce intervals with an error of order only $n^{-2}$. Necessary and sufficient conditions on the smoothing parameter,…

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Hierarchical Logistic Regression Models for Imputation of Unresolved Enumeration Status in Undercount Estimation

漏计估计中未确定登记状态插补的层级逻辑回归模型

In the process of collecting Post-Enumeration Survey (PES) data to evaluate census coverage, it is inevitable that there will be some individuals whose enumeration status (outcome in the census-PES match) remains unresolved even after extensive field follow-up operations. Earlier work developed a logistic regression framework for imputing the probability that unresolved individuals were enumerated in the census, so that the probability of having been enumerated is allowed to…

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On the Accuracy of Fieller Intervals for Binary Response Data

二元响应数据中 Fieller 区间的准确性

Finney proposed the use of a fiducial interval for the median response dose based on Fieller's theorem. An alternative is to use the asymptotic confidence interval. The simulations by Abdelbasit and Plackett suggest that the two intervals have similar coverage probabilities. We compare the two intervals theoretically and in an expanded simulation study. Our results show that Fieller intervals are generally superior. An attempt is made to characterize how and when the two…

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The Effect of Marijuana Decriminalization on Hospital Emergency Room Drug Episodes: 1975–1978

大麻非刑事化对医院急诊涉药事件的影响:1975—1978年

Between 1973 and 1978, 12 states with collectively over one-third of the total U.S. population enacted laws that decriminalized the possession of marijuana. This article uses standard metropolitan statistical area (SMSA) level data on hospital emergency room drug episodes collected by the Drug Abuse Warning Network to measure the effect of changes in drug penalties on substance abuse crises. The regression models demonstrate that marijuana decriminalization was accompanied…

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L 1 -Statistical Analysis and Related Methods.

L1 统计分析及相关方法

Prologue. Thoughts on Real Data and Statistics (Y. Dodge). Some Statistical Tools for the Analysis of Complex Molecular Spectra (A.F. Ruckstuhl). Estimation, Testing and Property Characterization. The Gauss Markov Property for the Median (G.W. Basset, Jr.,). A Class of Estimators Based on Adaptative Convex Combinations of Two Estimation Procedures (Y. Dodge, J. Jureckova). Robustness of L1 Regression in the Light of Linear Programming (J. Dupacova). A Linear Model with a…

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Sequential Labor Decisions Under Uncertainty: An Estimable Household Model of West-African Farmers

不确定性下的序贯劳动决策:西非农户的可估计家庭模型

This paper reports estimates of the primitive parameters of a stochastic control model describing the labor decisions of West African small farmers. Maximum likelihood estimates are computed although optimal labor decision rules cannot be derived analytically. Vuong's non-nested model specification test shows that this method yields estimates superior to those derived by assuming that farmers solve a deterministic control problem. Low levels of labor effort are shown to be a…

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Minimum MSE estimation of a regression model with fixed effects from a series of cross-sections

重复截面下固定效应回归模型的最小均方误差估计

If panel data are not available but repeated cross-sections are, the parameters in a regression model with fixed individual effects can be estimated consistently using the cohort approach proposed by Deaton (1985). In this paper we show that Deaton's estimator is inconsistent if the number of time periods is small, even if the number of cohorts tends to infinity. Moreover, we propose an alternative estimator which does not suffer from a bias due to a small number of sampling…

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Interpolated Nonparametric Prediction Intervals and Confidence Intervals

插值构造的非参数预测区间与置信区间

SUMMARY In several important statistical problems, prediction intervals and confidence intervals can be constructed with coverage levels which are known precisely but cannot be rendered equal to predetermined levels such as 0.95. One solution to this difficulty is to interpolate between such intervals. We show that simple linear interpolation reduces the order of coverage error, but that higher orders of interpolation produce no further improvement. The error is reduced by a…

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Conditional Choice Probabilities and the Estimation of Dynamic Models

This paper develops a new method for estimating the structural parameters of (discrete choice) dynamic programming problems. The method reduces the computational burden of estimating such models. We show the valuation functions characterizing the expected future utility associated with the choices often can be represented as an easily computed function of the state variables, structural parameters, and the probabilities of choosing alternative actions for states which are…

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Nonparametric identification and estimation of polychotomous choice models

多类别选择模型的非参数识别与估计

In this paper we provide conditions guaranteeing the identification of nonparametric polychotomous choice models. In these models, neither the subutility function of observable attributes nor the distribution of the unobservable random terms is specified parametrically. Sets of nonparametric functions that possess properties that are often implied by economic theory and satisfy the restrictions required to identify the models are described. We use the identification results…

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