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混合识别强度下的相关矩条件选择AI 解读

This paper proposes a robust moment selection method aiming to pick the best model even if this is a moment condition model with mixed identification strength, that is, moment conditions including moment functions that are local to zero uniformly over the parameter set. We show that the relevant moment selection procedure of Hall et al. (2007, Journal of Econometrics 138, 488–512) is inconsistent in this setting as it does not explicitly account for the rate of convergence…

随机子空间局部投影AI 解读

Abstract We show how random subspace methods can be adapted to estimating local projections with many controls. Random subspace methods have their roots in the machine learning literature and are implemented by averaging over regressions estimated over different combinations of subsets of these controls. We document three key results: (i) Our approach can successfully recover the impulse response functions across Monte Carlo experiments representative of different…

Wyse 等对《网络潜在增长过程的根节点与社区推断》的讨论AI 解读

We congratulate the authors on a thought provoking paper introducing innovative ideas for the statistical modelling of networks. The emphasis on identifying highly probable root nodes is both original and intriguing. In particular, using the root node credible set methods for node clustering within the PAPER(α,β,α0,θ) random K model (Section 6.3) is an interesting perspective. We wonder how these approaches might connect with latent space model-based node clustering methods…

随机非线性定义域上函数型数据的可解释判别分析：阿尔茨海默病应用AI 解读

Abstract We introduce a novel framework for the classification of functional data supported on nonlinear, and possibly random, manifold domains. The motivating application is the identification of subjects with Alzheimer’s disease from their cortical surface geometry and associated cortical thickness map. The proposed model is based upon a reformulation of the classification problem as a regularized multivariate functional linear regression model. This allows us to adopt a…

众多弱工具变量与无效工具变量下的工具变量估计AI 解读

Abstract We discuss the fundamental issue of identification in linear instrumental variable (IV) models with unknown IV validity. With the assumption of the ‘sparsest rule’, which is equivalent to the plurality rule but becomes operational in computation algorithms, we investigate and prove the advantages of non-convex penalized approaches over other IV estimators based on two-step selections, in terms of selection consistency and accommodation for individually weak IVs.…

GENIUS-MAWII：应对大量弱且无效工具变量的稳健孟德尔随机化AI 解读

Abstract Mendelian randomization (MR) addresses causal questions using genetic variants as instrumental variables. We propose a new MR method, G-Estimation under No Interaction with Unmeasured Selection (GENIUS)-MAny Weak Invalid IV, which simultaneously addresses the 2 salient challenges in MR: many weak instruments and widespread horizontal pleiotropy. Similar to MR-GENIUS, we use heteroscedasticity of the exposure to identify the treatment effect. We derive influence…

选择条件工具变量集合的图形工具AI 解读

Summary We consider the efficient estimation of total causal effects in the presence of unmeasured confounding using conditional instrumental sets. Specifically, we consider the two-stage least-squares estimator in the setting of a linear structural equation model with correlated errors that is compatible with a known acyclic directed mixed graph. To set the stage for our results, we characterize the class of linearly valid conditional instrumental sets that yield consistent…

利用离散时间观测估计连续时间线性 DSGE 模型AI 解读

We provide a general state space framework for estimation of the parameters of continuous-time linear DSGE models from discrete-time data. Our approach relies on the exact discrete-time representation of the equilibrium dynamics, hence avoiding discretization errors. We construct the exact likelihood for data sampled either as stocks or flows, based on the Kalman filter, and provide necessary and sufficient conditions for local identification of the frequency-invariant…

具有动态因子系数与条件异方差误差的向量自回归AI 解读

We introduce a new and general methodology for analyzing vector autoregressive models with time-varying coefficient matrices and conditionally heteroskedastic disturbances. The proposed approach is transparent and simple to implement. It allows the derivation of well-defined impulse response functions that rely on the overall stability of the system. We present the finite sample properties of the model in a simulation study. In an empirical illustration we investigate the…

局部投影与向量自回归：数千种数据生成过程的启示AI 解读

We conduct a simulation study of Local Projection (LP) and Vector Autoregression (VAR) estimators of structural impulse responses across thousands of data generating processes, designed to mimic the properties of the universe of U.S. macroeconomic data. Our analysis considers various identification schemes and several variants of LP and VAR estimators, employing bias correction, shrinkage, or model averaging. A clear bias–variance trade-off emerges: LP estimators have lower…

目标主成分分析：高维面板数据的迁移学习AI 解读

This paper develops a novel method to estimate a latent factor model for a large target panel with missing observations by optimally using the information from auxiliary panel data sets. We refer to our estimator as target-PCA. Transfer learning from auxiliary panel data allows us to deal with a large fraction of missing observations and weak signals in the target panel. We show that our estimator is more efficient and can consistently estimate weak factors, which are not…

非常规货币政策的准备金供给渠道AI 解读

We find that central bank reserves injected by QE crowd out bank lending. We estimate a structural model with cross-sectional instrumental variables for deposit and loan demand. Our results are determined by the elasticity of loan demand and the impact of reserve holdings on the cost of supplying loans. The reserves injected by QE raise loan rates by 7.4 basis points, and each dollar of reserves reduces bank lending by 7.7 cents. Our results imply that a large injection of…

参与情况误报时的项目收益识别AI 解读

Summary In cases of noncompliance with an assigned treatment, estimates of causal effects typically rely on instrumental variables (IV). However, when participation is also misreported, the IV estimand may become a nonconvex combination of local average treatment effects that fails to satisfy even a minimal condition for being causal. The aim of our paper is to generalize the MR‐LATE approach. This is an alternative IV estimand that is more robust in cases of noncompliance…

存在部分服从者时完整处理效应的工具变量估计AI 解读

Abstract The effect of the full treatment is a primary parameter of interest in policy evaluation, while often the effect of a subset of treatment is estimated. We partially identify the local average treatment effect of receiving full treatment (LAFTE) using an instrumental variable that may induce individuals into subsets of treatment (partial compliers). We show that partial compliers violate the standard exclusion restriction, necessary conditions on the absence of…

内生二元处理的无条件政策效应：无条件 MTE 方法AI 解读

This paper studies the identification and estimation of policy effects when treatment status is binary and endogenous. We introduce a new class of marginal treatment effects (MTEs) based on the influence function of the functional underlying the policy target. We show that an unconditional policy effect can be represented as a weighted average of the newly defined MTEs over the individuals who are indifferent about their treatment status. We provide conditions for point…

多值处理效应的高维模型辅助推断AI 解读

Consider estimation of average treatment effects with multi-valued treatments using augmented inverse probability weighted (IPW) estimators, depending on outcome regression and propensity score models in high-dimensional settings. These regression models are often fitted by regularized likelihood-based estimation, while ignoring how the fitted functions are used in the subsequent inference about the treatment parameters. Such separate estimation can be associated with known…

潜在效用与置换不变性：显示偏好方法AI 解读

This paper provides partial identification results for latent utility models that satisfy an invariance property on unobservables such as exchangeability. We employ a simple revealed preference argument to “difference out” unobservables, obtaining identifying inequalities for utility indices. We show the differencing argument is also useful for counterfactual analysis. The framework generalizes existing work in discrete choice by allowing latent feasibility sets and by…

不完全信息离散选择模型中的识别AI 解读

We study identification of preferences in static single-agent discrete choice models where decision makers may be imperfectly informed about the state of the world. Leveraging the notion of one-player Bayes Correlated Equilibrium by Bergemann and Morris (2016), we provide a tractable characterisation of the sharp identified set. We develop a procedure to practically construct the sharp identified set following a sieve approach, and provide sharp bounds on counterfactual…

高维面板数据模型的 GMM 估计AI 解读

In this paper, we study a class of high dimensional moment restriction panel data models with interactive effects, where the factors are unobserved and these factor loadings are nonparametrically unknown smooth functions of individual characteristic variables. We allow the dimension of the parameter vector and the number of moment conditions to diverge with the sample size. This is a very general framework and is closely related to many existing linear and nonlinear panel…

利用不完美参照检测衡量诊断性能：部分识别方法AI 解读

Diagnostic tests are almost never perfect. Studies quantifying their performance use knowledge of the true health status, measured with a reference diagnostic test. Researchers commonly assume that the reference test is perfect, which is often not the case in practice. When the assumption fails, conventional studies identify “apparent” performance or performance with respect to the reference, but not true performance. This paper provides the smallest possible bounds on the…

GMM 推断：部分识别、识别强度与非标准渐近理论AI 解读

This paper analyses aspects of generalized method of moments (GMM) inference in moment equality models in settings where standard regularity conditions may break down. Explicit analytic formulations for the asymptotic distributions of estimable functions of the GMM estimator and statistics based on the GMM criterion function are derived under relatively mild assumptions. The moment Jacobian is allowed to be rank deficient, so first order identification may fail, the values…

含动态潜在因子的小维度 VAR 模型中的工具变量推断AI 解读

We study semiparametric inference in a small-dimensional vector autoregressive (VAR) model of order p augmented by unobservable common factors with a dynamic described by a VAR process of order q . This state-space specification is useful to measure separately the direct causality effects and the responses to dynamic common factors. We show that the state-space parameters are identifiable from the autocovariance function of the observed process. We estimate the model by…

无效工具变量下 IV 估计量的高阶近似AI 解读

This paper analyzes the higher-order approximation of instrumental variable (IV) estimators in a linear homoskedastic IV regression model when a large set of instruments with potential invalidity is present. We establish theoretical results on the higher-order mean-squared error (MSE) approximation of the two-stage least-squares (2SLS), the limited information maximum likelihood (LIML), the Fuller (FULL), the bias-adjusted 2SLS, and jackknife version of the LIML and FULL…

恐怖主义与教育：工具变量估计的证据AI 解读

Summary This paper estimates the effect of exposure to terrorist violence on education. Since terrorists may choose targets endogenously, we construct a set of novel instruments. To that end, we leverage exogenous variation from a local terrorist group's revenues and its affiliation with al‐Qaeda. Across several Kenyan datasets, we find that attacks suppress school enrolment more than predicted by difference‐in‐differences‐type estimators. This indicates that terrorists…