论文检索

Two frequent approaches for identifying structural VARs are external instruments, which carry economic content but are often weak, and non-Gaussianity of the shocks, which provides statistical identification but carries no economic meaning. We combine the two strategies in a single generalized method of moments framework. This hybrid approach point-identifies the target shocks while also identifying the non-target shocks up to sign and ordering. Under suitable rank…

有意过度识别的局部投影AI 解读

Local projections (LPs) have become a standard tool for estimating dynamic effects in macroeconomics and are typically implemented using ordinary least squares (OLS) for a given lag order. We propose an alternative approach that exploits additional lags as excluded instrumental variables (IVs). Under a finite-order model with martingale-difference shocks, the resulting (optimally weighted) IV estimator is never asymptotically less efficient than OLS and, as we show, can…

IV-LASSO 的工具变量字典与惩罚选择AI 解读

Estimating the first stage of an instrumental variables (IV) model with the least absolute shrinkage and selection operator (LASSO) requires choosing a dictionary of technical instruments and a penalty level. First-order asymptotic theory offers no guidance on these choices, as any consistent implementation yields a structural parameter estimator with the same limiting distribution. In finite samples, however, these choices can have a substantial impact on the resulting…

非线性矩模型的高效随机广义矩估计AI 解读

We study the asymptotic properties of the nonlinear stochastic GMM algorithm proposed by Chen, Lee, Liao, Seo, Shin and Song for recursively estimating parameters defined by possibly nonlinear and overidentified moment restrictions $\E[g(X,β^*)]=0$. Each observation updates estimates of the moment Jacobian, inverse covariance matrix and Gauss--Newton preconditioner at a cost independent of sample size. For linear and nonlinear instrumental-variable moments, we derive an…

嵌套工具变量分析：转换者平均处理效应及可推广性AI 解读

Instrumental variables (IVs) are widely used to estimate causal effects from non-randomized data. A canonical example is a randomized trial with noncompliance, in which the randomized treatment assignment serves as an IV for the non-ignorable treatment received. Under a monotonicity assumption, a valid IV nonparametrically identifies the average treatment effect among a latent complier subgroup, whose generalizability is often under debate. In many studies, there exist…

借助知识迁移增强代表性不足子群体的工具变量分析AI 解读

Instrumental variable (IV) analysis in underrepresented target populations suffers from low statistical efficiency and weak IV bias due to limited sample size. Leveraging external knowledge from a source population with larger samples offers an appealing solution to this problem. We propose in this paper the KNowledge-powered IV Estimator for underrepresented Subpopulations (KNIVES), a framework that enhances the IV analysis for underrepresented subpopulations by…

Experiments with spillovers create incentives for strategic behavior: when one household member's treatment can affect another, take-up decisions become interdependent. We propose an instrumental-variables framework grounded in game theory, in which take-up is a Nash equilibrium among a small number of agents, and all variables are discrete. Under minimal assumptions, without specifying how equilibria are selected, we derive sharp non-parametric bounds for direct, indirect,…

Instrumental variable (IV) methods mitigate bias from unobserved confounding in observational causal inference but rely on the availability of a valid instrument, which can often be difficult or infeasible to identify in practice. In this paper, we propose a representation learning approach that constructs instrumental representations from observed covariates, which could enable IV-based estimation even in the absence of an explicit instrument. Our model (ZNet) achieves this…

商品风险的构成：微观、市场与全经济来源AI 解读

We study the anatomy of commodity risk by distinguishing micro, market-level, and economy-wide sources. We develop a two-stage "divide-and-conquer" framework that allows sensitivities to these risk sources to vary across commodities while treating economy-wide risk as latent. The first stage uses defactored instrumental-variable estimation to recover commodity-specific sensitivities to micro and market conditions. The second combines principal components with…

熵正则化最优传输下的部分识别AI 解读

In many statistical settings, the available data and maintained assumptions do not suffice to uniquely identify the model parameters of interest. In such cases, one can only identify sets which are guaranteed to contain the true parameters. These are often characterized through linear programs that optimize over models compatible with the observed data. These programs can be infinite-dimensional in the optimizer and the number of constraints. We provide a unified way to…

挖掘因果关系：人工智能辅助搜索工具变量AI 解读

The instrumental variables (IVs) method is a leading empirical strategy for causal inference. Finding IVs is a heuristic and creative process, and justifying their validity---especially exclusion restrictions---is largely rhetorical. We propose using large language models (LLMs) to search for new IVs through narratives and counterfactual reasoning, similar to how a human researcher would. The stark difference, however, is that LLMs can dramatically accelerate this process…

大面板中的颗粒工具变量：强、近弱与弱工具下的识别和推断AI 解读

I develop asymptotic theory for Granular Instrumental Variables (GIV) in large panels with both $N$ and $T$ growing. The granular instrument is constructed from individual shocks that must be estimated from the panel. I show that this estimation has a first-order effect on the asymptotic variance of the GIV estimator. Existing methods do not account for this effect, leading to incorrect standard errors and invalid inference. I derive corrected standard errors that deliver…

全正编码下的 INSIDE 假设：解释与部分经验评估AI 解读

Mendelian randomisation (MR) implemented through instrumental variable (IV) analysis is a popular strategy for strengthening causal inference in observational studies. A key assumption for several MR estimators, including MR-Egger regression, is the INstrument Strength Independent of Direct Effect (INSIDE) assumption. However, there is no established empirical test for assessing the plausibility of this assumption. Moreover, INSIDE depends on how genetic variants are coded…

识别临床AI预测多重性中亚组脆弱性的统计框架AI 解读

AI models trained on the same data can disagree about patient risk, with disagreement potentially concentrated in clinically important subgroups. We propose V(S), a statistically grounded vulnerability index combining an observable lower-bound witness of model disagreement with clinical severity, and develop inference and multiplicity-adjustment procedures for auditing prespecified subgroups. We applied the framework to two large critical-care cohorts, MIMIC-IV (n = 65,078)…

工具变量回归中维度自适应的自助法 Anderson–Rubin 检验AI 解读

Weak-identification-robust tests for instrumental variable (IV) regressions are typically developed separately depending on whether the number of IVs is treated as fixed or increasing with the sample size, forcing researchers to make a stance on the asymptotic behavior, which is often ambiguous in practice. This paper proposes a bootstrap-based, dimension-agnostic Anderson-Rubin (AR) test that achieves correct asymptotic size regardless of whether the number of IVs is fixed…

线性IV模型中持续更新GMM的多项式方法AI 解读

This paper characterizes the global minimum of the continuously updating generalized method of moments (CU-GMM) objective in linear instrumental variables models. We allow optimal weighting matrices under heteroskedasticity, autocorrelation, or clustering. We show that the objective is a ratio of polynomials. For one endogenous regressor, stationary points of CU-GMM objective function are real eigenvalues of a companion matrix. Comparing their objective values with the value…

BGM-IV：面向高维协变量工具变量回归的贝叶斯生成建模AI 解读

Instrumental-variable (IV) regression enables causal estimation under endogeneity, but modern IV problems often involve nonlinear structural effects and high-dimensional covariates. Existing methods typically operate in observed or generic learned feature spaces, and they often yield point estimates without uncertainty quantification. We introduce BGM-IV, a Bayesian generative modeling approach that performs nonlinear IV regression through posterior inference in a causally…

多元结果非可分工具变量模型的识别与估计AI 解读

This paper proposes a novel nonseparable instrumental variable model to address endogeneity in settings with multivariate outcomes. While traditional approaches typically estimate separate one-dimensional quantile models for each outcome, the inherent correlation among different dimensions frequently invalidates their fundamental assumptions. To overcome this limitation, our framework jointly models the distribution of multiple outcomes. By assuming that the structural…

线性不等式约束下投影几何的极小极大选择AI 解读

Economic theory frequently implies linear inequality restrictions on parameters or functions of interest. A common way to impose such restrictions is to project an unrestricted estimator onto the feasible set. Projection estimators arise naturally from constrained least squares, instrumental variables, generalized method of moments, maximum likelihood, and related extremum procedures. When the sampling covariance, loss function, and projection criterion induce different…

样本量依赖核的高维加权与双样本 U 统计量高斯近似AI 解读

We develop Gaussian approximations for high-dimensional vectors formed by second-order $U$-and $V$-statistics whose kernels depend on sample size. To accommodate weighted and two-sample $U$-and $V$-statistics within a unified framework, we establish our results under independent but not identically distributed (i.n.i.d.) sampling. The resulting theorems apply to a broad range of problems, including estimation in linear regression models with many covariates or many-weak…

福利风险：政策评价的分布稳健界AI 解读

This paper develops a framework to study how the welfare effects of policy interventions are distributed across individuals when those effects are not directly observed. We bound the superquantile of individual welfare changes by the superquantile of conditional average welfare changes, which is typically identified, revealing how gains and losses are spread across the population. We also bound the share of the population whose welfare loss from the policy exceeds any given…

两样本孟德尔随机化中更具可信度的点识别假设AI 解读

Two-sample Mendelian randomization (MR) is a widely applied methodology in epidemiology. In two-sample MR, summary data (typically, regression coefficients and standard errors) quantifying the association between multiple genetic variants and the exposure and the outcome are used in an instrumental variable framework aimed at estimating the causal effect of the exposure on the outcome. Most two-sample MR methods were developed under data-generating models where the…

不完全依从下利用树方法估计异质性因果效应AI 解读

We study the impact of conditional complier average causal effect (CCACE) estimation methods on the performance of subgroup discovery and heterogeneous causal effect estimation under imperfect compliance. Building on the Bayesian Causal Forest with Instrumental Variable (BCF-IV) (Bargagli-Stoffi et al. (2022)) method, we introduce a two-step, model-agnostic approach that allows any suitable machine learning method to be used for the CCACE estimation in the first step.…

观察性时间序列动态因果效应的半参数推断AI 解读

In observational time series, statistical inference for dynamic causal effects of a one-time intervention across horizons is complicated by high-dimensional observed pre-treatment information, unmeasured confounding, and serial dependence. To address these challenges, we develop a semiparametric framework for inference from a single serially dependent time series, integrating debiased machine learning with instrumental variables through buffered block cross-fitting. Under…