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有序结果中内生变量效应的估计AI 解读

Abstract We examine the use of instrumental variable (IV) methods to measure the effect of a ceteris paribus change in an endogenous variable on an ordered outcome. Specifically, we use these methods to investigate the effect of neighborhood characteristics on subjective well-being (SWB) among participants in the Moving to Opportunity (MTO) housing voucher experiment. We find that the estimated positive effect of a decrease in neighborhood poverty on SWB is sensitive to the…

拓展工具变量方法的适用范围AI 解读

Summary Since Philip Wright’s proposal of the linear instrumental variables (IV) estimator in 1928, the scope of application of IV models has greatly expanded. There are now nonlinear IV models in use with both additive and nonadditive latent variables, employing both semiparametric and nonparametric specifications. A notable feature of Wright’s linear IV estimator is that it is applicable to incomplete models that leave the genesis of endogenous explanatory variables…

Tobit模型的工具变量方法AI 解读

This paper studies models of processes generating censored outcomes with endogenous explanatory variables and instrumental variable restrictions. Tobit-type left censoring at zero is the primary focus in the exposition. Extension to stochastic censoring is sketched. The models do not specify the process determining endogenous explanatory variables and they do not embody restrictions justifying control function approaches. Consequently, they can be partially or point…

理解测量误差对分位数回归的影响AI 解读

The impact of measurement error in explanatory variables on quantile regression functions is investigated using a small variance approximation. The approximation shows how the error contaminated and error free quantile regression functions are related. A key factor is the distribution of the error free explanatory variable. Exact calculations probe the accuracy of the approximation. The order of the approximation error is unchanged if the density of the error free…

广义工具变量模型AI 解读

This paper develops characterizations of identified sets of structures and structural features for complete and incomplete models involving continuous or discrete variables.Multiple values of unobserved variables can be associated with particular combinations of observed variables.This can arise when there are multiple sources of heterogeneity, censored or discrete endogenous variables, or inequality restrictions on functions of observed and unobserved variables.The models…

二元结果的工具变量随机系数模型AI 解读

In this paper, we study a random‐coefficients model for a binary outcome. We allow for the possibility that some or even all of the explanatory variables are arbitrarily correlated with the random coefficients, thus permitting endogeneity. We assume the existence of observed instrumental variables Z that are jointly independent with the random coefficients, although we place no structure on the joint determination of the endogenous variable X and instruments Z, as would be…

协变量测量误差下的处理效应估计AI 解读

This paper investigates the effect that covariate measurement error has on a treatment effect analysis built on an unconfoundedness restriction in which there is conditioning on error free covariates. The approach uses small parameter asymptotic methods to obtain the approximate effects of measurement error for estimators of average treatment effects. The approximations can be estimated using data on observed outcomes, the treatment indicator and error contaminated…

多项离散选择的工具变量模型AI 解读

This paper studies identification of latent utility functions in multiple discrete choice models in which there may be endogenous explanatory variables, that is explanatory variables that are not restricted to be distributed independently of the unobserved determinants of latent utilities. The model does not employ large support, special regressor or control function restrictions, indeed it is silent about the process delivering values of endogenous explanatory variables and…

二元响应半参数结构模型：形状限制与部分识别AI 解读

I study the partial identifying power of structural single-equation threshold-crossing models for binary responses when explanatory variables may be endogenous. The sharp identified set of threshold functions is derived for the case in which explanatory variables are discrete, and I provide a constructive proof of sharpness. There is special attention to a widely employed semiparametric shape restriction, which requires the threshold-crossing function to be a monotone…

有序选择的工具变量模型AI 解读

This paper studies single equation instrumental variable models of ordered choice in which explanatory variables may be endogenous. The models are weakly restrictive, leaving unspecified the mechanism that generates endogenous variables. These incomplete models are set, not point, identifying for parametrically (e.g. ordered probit) or nonparametrically specified structural functions. The paper gives results on the properties of the identified set for the case in which…

额外异质性、内生性与指数限制AI 解读

A discrete or continuous outcome is determined by a structural function in which the effect of some variables of interest is transmitted through a scalar index. Multiple sources of stochastic variation can appear as arguments of the structural function, but not in the index. There may be endogeneity, that is observable and unobservable variables may not be independently distributed. Conditions are provided under which there is local identification of measures of the relative…

离散变化下的非参数识别AI 解读

This paper provides weak conditions under which there is nonparametric interval identification of local features of a structural function that depends on a discrete endogenous variable and is nonseparable in latent variates. The function delivers values of a discrete or continuous outcome and instruments may be discrete valued. Application of the analog principle leads to quantile regression based interval estimators of values and partial differences of structural functions.…

不可分离模型中的识别AI 解读

Weak nonparametric restrictions are developed, sufficient to identify the values of derivatives of structural functions in which latent random variables are nonseparable. These derivatives can exhibit stochastic variation. In a microeconometric context this allows the impact of a policy intervention, as measured by the value of a structural derivative, to vary across people who are identical as measured by covariates. When the restrictions are satisfied quantiles of the…

从家庭总量推断个人需求：膳食构成的时间与年龄变化AI 解读

This paper uses non-parametric regression with smoothness induced by imposition of a roughness penalty to estimate time-dependent relationships between age and intakes of energy, total fat and saturated fatty acids employing data on household food acquisitions from a continuous survey covering the period 1975-94. A discrete analogue of the bivariate thin plate spline smoother is used to smooth the estimates across age and time. With age and calendar time-specific estimated…

从家庭总量恢复个人需求：膳食构成的时间与年龄变化AI 解读

This paper uses non-parametric regression with smoothness induced by imposition of a roughness penalty to estimate time-dependent relationships between age and intakes of energy, total fat and saturated fatty acids employing data on household food acquisitions from a continuous survey covering the period 1975-94. A discrete analogue of the bivariate thin plate spline smoother is used to smooth the estimates across age and time. With age and calendar time-specific estimated…

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保留工资的计量经济学分析AI 解读

This paper describes an analysis of survey data on unemployed people who reported their asking wage and the wage they expected to earn. We show that if agents' behavior is described by the standard optimal job search model, one can use these data to deduce structural parameters rather than estimate them, and we report our deductions from two surveys^1 of British unemployed people.