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Abstract It is well-known that individual beliefs cannot be identified using traditional choice data, unless we exogenously assume state-independent utilities. In this paper, I propose a novel methodology that solves this long-standing identification problem in a simple way. This method relies on extending the state space by introducing a proxy, for which the agent has no stakes conditional on the original state space. The latter allows us to identify the agent’s conditional…

最优治疗规则的改进界与推断AI 解读

Point identification of causal effects requires strong assumptions that are unreasonable in many practical settings. However, informative bounds on these effects can often be derived under plausible assumptions. Even when these bounds are wide or cover null effects, they can guide practical decisions based on formal decision theoretic criteria. Here we derive new results on optimal treatment regimes in settings where the effect of interest is bounded. These results are…

无需指定网络结构的同伴效应检验AI 解读

This paper proposes an Anderson–Rubin (AR) test for the presence of peer effects in panel data without the need to specify the network structure. The unrestricted model of our test is a linear panel data model of social interactions with dyad-specific peer effect coefficients for all potential peers. The proposed AR test evaluates if these peer effect coefficients are all zero. As the number of peer effect coefficients increases with the sample size, so does the number of…

检验预测效率的工具变量方法AI 解读

ABSTRACT We study a specific form of forecast efficiency that requires forecast errors to be unpredictable from forecast revisions. One approach aggregates forecasts and estimates an aggregated efficiency regression, while another estimates the relationship by running separate regressions for each individual and then aggregating. We demonstrate that both estimators can be asymptotically biased in the presence of public noise. To address these biases, we propose instrumenting…

工具变量模型中的识别：Abadie 的 Kappa 的核心作用AI 解读

We study instrumental variables models characterized by: (i) Unobserved heterogeneity consisting of potential outcomes and response types that describe how the instrument determines treatment choice; (ii) Conditional independence of the instrument and the unobserved heterogeneity; and (iii) Convex restrictions on the distribution of unobserved heterogeneity. We show certain causal parameters are identified in these models if and only if a version of the kappa of Abadie…

农村移民与城市非正规就业：来自巴西的证据AI 解读

This paper studies the economic effects of rural‐urban migration on Brazilian cities. Using a shift‐share IV design, we show that, over a decade, drought‐induced immigration reduces informality, has no effect on unemployment, and increases the number of formal firms and jobs. Downward formal wage adjustments play a key role, as these long‐run effects are weaker in regions with stronger wage rigidity. In the short run, when wage rigidity is strongest, we replicate the…

完备性是否必要？未识别线性模型中的估计AI 解读

Modern data analysis depends increasingly on estimating models via flexible high-dimensional or nonparametric machine learning methods, where the identification of structural parameters is often challenging and untestable. In linear settings, this identification hinges on the completeness condition, which requires the nonsingularity of a high-dimensional matrix or operator and may fail for finite samples or even at the population level. Regularized estimators provide a…

函数型工具变量回归及移民对本土工资影响的估计AI 解读

Functional linear regression has gained popularity as a statistical tool for studying the relationship between function-valued variables. However, in practice, it is hard to expect that the explanatory variables of interest are strictly exogenous, due to, for example, the presence of omitted variables and measurement error. This issue of endogeneity remains insufficiently explored, in spite of its empirical importance. To fill this gap, this article proposes new consistent…

通过边际处理效应进行福利分析AI 解读

We consider a causal structure with endogeneity, i.e., unobserved confoundedness, where an instrumental variable is available. In this setting, we show that the mean social welfare function can be identified and represented via the marginal treatment effect as the operator kernel. This representation result can be applied to a variety of statistical decision rules for treatment choice, including plug-in rules, Bayes rules, and empirical welfare maximization rules. Focusing…

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…

协变量辅助的工具变量因果效应界限AI 解读

Abstract When an exposure of interest is confounded by unmeasured factors, an instrumental variable (IV) can be used to identify and estimate certain causal contrasts. Identification of the marginal average treatment effect (ATE) from IVs relies on strong untestable structural assumptions. When one is unwilling to assert such structure, IVs can nonetheless be used to construct bounds on the ATE. Famously, Alexander Balke and Judea Pearl proved tight bounds on the ATE for a…

含非参数内生效应的空间自回归面板模型估计AI 解读

This paper proposes a sieve generalized method of moments (GMM) method for the estimation of spatial autoregressive panel data models with nonparametric endogenous effect. The new estimator incorporates both linear moments based on the orthogonality of the exogenous regressors with the model disturbances and quadratic moments based on the properties of idiosyncratic errors. We establish the consistency and asymptotic normality of the sieve GMM estimator and show that it is…

差分带来的差异：观测冲击的局部投影应使用水平还是差分？AI 解读

ABSTRACT We show there are large finite‐sample estimation improvements from estimating local projections (LP) in a cumulated differences (long‐differenced) specification vs. a specification in levels when the impulse response of interest is to an externally identified (“observed”) shock. The long‐differenced specification substantially reduces, and in many cases eliminates, estimation bias, as well as significantly improves confidence interval coverage. These improvements…

积分方程解的线性泛函置信集合的渐近有效性AI 解读

Summary This paper examines the construction of confidence sets for parameters defined as linear functionals of a function of $ W $ and $ X $ whose conditional mean given $ Z $ and $ X $ equals the conditional mean of another variable $ Y $ given $ Z $ and $ X $. Many estimands of interest in causal inference can be expressed in this form, including the average treatment effect in proximal causal inference and treatment effect contrasts in instrumental variable models. We…

更稳健的多变量孟德尔随机化方法AI 解读

Summary Multivariable Mendelian randomization uses genetic variants as instrumental variables to infer the direct effects of multiple exposures on an outcome. However, unlike univariable Mendelian randomization, multivariable Mendelian randomization often faces greater challenges with many weak instruments, which can lead to bias not necessarily toward zero and inflation of Type-I errors. In this work, we introduce a new asymptotic regime that allows exposures to have…

用于政策学习的半参数工具变量双重差分方法AI 解读

Summary Recently, there has been a surge in methodological development for the difference-in-differences approach to evaluate causal effects. Standard methods in the literature rely on the parallel-trend assumption to identify the average treatment effect on the treated. However, the parallel-trend assumption may be violated in the presence of unmeasured confounding, and the average treatment effect on the treated may not be useful in learning a treatment assignment policy…

早产儿观察研究中的工具变量强化：设计、界限与推断AI 解读

Regionalization of intensive care for premature infants refers to a triage system that directs mothers to hospitals with varying capabilities based on the risks their babies face. Given the limited capacity of highly specialized hospitals, understanding the impact of delivering premature infants at these facilities on infant mortality could facilitate the design of a more efficient perinatal regionalization system. To address this, Baiocchi et al. proposed strengthening a…

高维弱工具变量下的推断与新凯恩斯菲利普斯曲线AI 解读

Inference on macroeconomic relations using instrumental variables (IVs) is often characterised by weak and high-dimensional IVs. I propose a weak-IV robust method that remains valid under dependent data, arbitrarily weak identification, and a number of IVs that can increase exponentially with the sample size. I show by simulation that using methods that are not robust to weak and high-dimensional IVs can lead to substantially biased inference, while the proposed test has…

Summary In an empirical study of persuasion, researchers often use a binary instrument to encourage individuals to consume information and take some action. We show that, with a binary Imbens–Angrist instrumental variable model and the monotone treatment response assumption, it is possible to identify the joint distribution of potential outcomes among compliers. This is necessary to identify the percentage of mobilized voters and their statistical characteristic defined by…

多重结构突变与多因子误差下空间面板模型的收缩估计AI 解读

This study investigates spatial panel data models with a multifactor error structure and multiple structural breaks occurring in the coefficients of both spatial lagged and explanatory variables. While extensive research has addressed cross-sectional dependence in panel data, including approaches that integrate spatial and factor structures within a single framework, few studies account for time-varying model parameters and achieving consistent estimation remains a…

群组层面处理的分位数回归AI 解读

To study distributional effects of group level treatments, Chetverikov et al. (2016) proposed a grouped instrumental variables quantile regression estimator, a quantile extension of the Hausman and Taylor’s 1981 instrumental variables estimator for panel data. However, their approach only allows for heterogenous distributional effects of group-level treatments that correspond to individual-level unobserved characteristics, but not group-level unobserved characteristics. In…

未知异方差或工具变量下的高维二元选择模型AI 解读

This paper proposes a new method for estimating high-dimensional binary choice models. We consider a semiparametric model that places no distributional assumptions on the error term, allows for heteroskedastic errors, and permits endogenous regressors. Our approaches extend the special regressor estimator originally proposed by Lewbel (2000). This estimator becomes impractical in high-dimensional settings due to the curse of dimensionality associated with high-dimensional…

利用边际处理效应开展分层政策评估AI 解读

This paper proposes a unified approach to derive sharp bounds on conventional policy parameters when the instrumental variables (IVs) are potentially invalid. Using a vine copula approach, we propose a novel characterization of the identified sets for the marginal treatment effect (MTE) and the policy-relevant treatment effect (PRTE) parameters. Our method has various advantages: First, it explicitly demonstrates how imposing different IV-related assumptions with different…

分位数回归估计量的偏差修正AI 解读

We study the bias of classical quantile regression and instrumental variable quantile regression estimators. While being asymptotically first-order unbiased, these estimators can have non-negligible second-order biases. We derive a higher-order stochastic expansion of these estimators using empirical process theory. Based on this expansion, we derive an explicit formula for the second-order bias and propose a feasible bias correction procedure that uses finite-difference…