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价格对分析师现金流预期的影响：主观信念数据与理性贴现率变动的协调AI 解读

I show that prices impact analyst cash flow expectations and argue this impact can partially reconcile subjective beliefs data with asset pricing models in which investors have rational expectations and discount rate variation drives prices. Previous work argues that correlations of biased analyst cash flow expectations with prices and future returns contradict rational models and imply biased investor expectations distort prices. However, using two instrumental variables…

政策相关处理参数对单调性违背的敏感性AI 解读

ABSTRACT This paper proposes a method to bound policy‐relevant treatment parameters (PRTPs) when the monotonicity assumption that the IV affects individuals' treatment response in the same direction is weakened. The bounding framework uses the proportion of defiers relative to compliers as a sensitivity parameter and yields an identified set that is an interval. The method is illustrated in an empirical application where the same‐sex instrument was used to calculate the…

结合代理变量与叙事符号限制：重新考察技术冲击的影响AI 解读

ABSTRACT This paper proposes a novel integration of the proxy structural vector autoregression approach with narrative sign restrictions to identify multiple shocks with multiple instrumental variables. We show that this combination leads to informative inferences on shock effects through both analytical demonstrations and Monte Carlo simulations. Applying this method to identify anticipated and unanticipated technology shocks, we find that anticipated shocks account for…

含因子面板模型中工具变量估计量的组合AI 解读

We address the estimation of factor-augmented panel data models using observed measurements to proxy for unobserved factors or loadings and explore the use of internal instruments to address the resulting endogeneity.The main challenge consists in that economic theory rarely provides insights into which measurements to choose as proxies when several are available.To overcome this problem, we propose a new class of estimators that are linear combinations of instrumental…

利用工具变量校正二元解释变量的误分类AI 解读

Estimators that exploit an instrumental variable to correct for misclassification in a binary regressor typically assume that the misclassification rates are invariant across all values of the instrument.We show this assumption is invalid in routine empirical settings.We derive a new estimator which allows misclassification rates to vary across values of the instrumental variable.Our key identifying assumption, that the sum of misclassification rates remains constant across…

配对实验中不完全依从下的推断AI 解读

This article studies inference for the local average treatment effect in randomized controlled trials with imperfect compliance where treatment status is determined according to “matched pairs.” By “matched pairs,” we mean that units are sampled iid from the population of interest, paired according to observed, baseline covariates and finally, within each pair, one unit is selected at random for treatment. Under weak assumptions governing the quality of the pairings, we…

成对有效的工具变量AI 解读

Finding valid instruments is difficult. We propose Validity Set Instrumental Variable (VSIV) estimation, a method for estimating local average treatment effects (LATEs) in heterogeneous causal effect models when the instruments are partially invalid. We consider settings with pairwise valid instruments, that is, instruments that are valid for a subset of instrument value pairs. VSIV estimation exploits testable implications of instrument validity to remove invalid pairs and…

2024年 Denis Sargan 计量经济学奖AI 解读

The editors1 of The Econometrics Journal decided to award the 2024 Denis Sargan Econometrics Prize to Rahul Singh (Harvard) and Liyang Sun (CEMFI and UCL) for their article “Double robustness for complier parameters and a semi-parametric test for complier characteristics” in the January 2024 issue of The Econometrics Journal.2 Suppose that we are interested in the effects of a binary treatment on some outcome and allow these effects to vary arbitrarily in the studied…

GMM 模型中正态近似的一般诊断方法AI 解读

Summary This paper proposes a diagnostic method for evaluating the quality of the normal approximation in generalized method of moments (GMM) models through sampling from a quasi-Bayesian parameter distribution. GMM estimates are consistent and asymptotically normal under certain regularity conditions, and researchers often assume normality when conducting inference. However, the literature has identified several violations to the normal approximation, such as in cases with…

一步平滑样条工具变量回归AI 解读

Summary We extend nonparametric regression smoothing splines to a context where there is endogeneity and instrumental variables are available. Unlike popular existing estimators, the resulting estimator is one step and relies on a unique regularisation parameter. We derive rates of the convergence for the estimator and its first derivative, which are uniform in the support of the endogenous variable. We also address the issue of imposing monotonicity in estimation and extend…

不可观测因素是否可分离？AI 解读

It is common to assume in empirical research that observables and unobservables are additively separable, especially when the former are endogenous. This is because it is widely recognized that identification and estimation challenges arise when interactions between the two are allowed for. Starting from a nonseparable IV model, where the instrumental variable is independent of unobservables, we develop a novel nonparametric test of separability of unobservables. The…

处理交互作用与内生参与AI 解读

ABSTRACT We study causal inference in randomized experiments (or quasi‐experiments) following a factorial design. There are two treatments, denoted and , and units are randomly assigned to one of four categories: treatment alone, treatment alone, joint treatment, or none. Allowing for endogenous non‐compliance with the two binary instruments representing the intended assignment, as well as unrestricted interference across the two treatments, we derive the causal…

不确定性冲击的宏观响应：递归排序的风险AI 解读

ABSTRACT A common practice in empirical macroeconomics is to examine alternative recursive orderings of the variables in structural vector autoregressive (VAR) models. When the implied impulse responses look similar, the estimates are considered trustworthy. When they do not, the estimates are used to bound the true response without directly addressing the identification challenge. A leading example of this practice is the literature on the effects of uncertainty shocks on…

簇刀切工具变量估计AI 解读

Abstract Researchers commonly use jackknife-based instrumental variables estimators to eliminate the many-instruments bias of two-stage least squares. Where inference must be clustered, however, the jackknife fails to eliminate the bias. We propose a cluster-jackknife approach in which first-stage predicted values for each observation are constructed from a regression that leaves out the observation’s entire cluster. The cluster-jackknife instrumental variables estimator…

持续性与异方差预测变量下的预测分位数回归：高功效的两阶段最小二乘检验方法AI 解读

Funding Information: The authors thank the Co-Editor, Michael Jansson, an Associate Editor and two anonymous referees for their helpful and constructive comments. We also thank participants of the IAAE Annual conference 2023 at the BI Norwegian Business School, Oslo, Norway, and seminar participants at the University of the Balearic Islands, Palma de Maiorca, Spain for their comments. Demetrescu gratefully acknowledges the support of the Deutsche Forschungsgemeinschaft (DFG,…

仿射模型中尖锐识别集的简易子向量推断AI 解读

This paper studies a regularized support function estimator for bounds on components of the parameter vector in the case in which the identified set is a polygon. The proposed regularized estimator has three important properties: (i) it has a uniform asymptotic Gaussian limit in the presence of flat faces in the absence of redundant (or overidentifying) constraints (or vice versa); (ii) the bias from regularization does not enter the first-order limiting distribution; (iii)…

使用非线性筛方法推断时间序列非参数条件矩限制AI 解读

This paper studies estimation of and inference on dynamic nonparametric conditional moment restrictions of high dimensional variables for weakly dependent data, where the unknown functions of endogenous variables can be approximated via nonlinear sieves such as neural networks and Gaussian radial bases. The true unknown functions and their sieve approximations are allowed to be in general weighted function spaces with unbounded supports, which is important for time series…

非参数时变工具变量结构 VAR：估计与推断AI 解读

Abstract This paper studies the estimation and inference of time-varying impulse response functions in structural vector autoregressions (SVARs) identified with external instruments. Building on kernel estimators that allow for nonparametric time variation, we derive the asymptotic distributions of the relevant quantities. Our estimators are simple and computationally trivial and allow for potentially weak instruments. Simulations suggest satisfactory empirical coverage even…

孟德尔随机化中检验双向因果效应的聚焦框架AI 解读

Abstract Mendelian randomization (MR) is a powerful method that uses genetic variants as instrumental variables to infer the causal effect of a modifiable exposure on an outcome. We study inference for bi-directional causal relationships and causal directions with possibly pleiotropic genetic variants. We show that assumptions for common MR methods are often impossible or too stringent given the potential bi-directional relationships. We propose a new focusing framework for…

线性工具变量回归的稳健置换检验AI 解读

This paper develops permutation versions of identification-robust tests in linear instrumental variables (IV) regression. Unlike the existing randomization and rank-based tests in which independence between the instruments and the error terms is assumed, the permutation Anderson- Rubin (AR), Lagrange Multiplier (LM) and Conditional Likelihood Ratio (CLR) tests are asymptotically similar and robust to conditional heteroskedasticity under standard exclusion restriction i.e.…

高维协变量下对异方差稳健的过度识别约束检验AI 解读

This paper proposes an overidentifying restriction test for high-dimensional linear instrumental variable models. The novelty of the proposed test is that it allows the number of covariates and instruments to be larger than the sample size. The test is scale-invariant and robust to heteroskedastic errors. To construct the final test statistic, we first introduce a test based on the maximum norm of multiple parameters that could be high-dimensional. The theoretical power…

工具变量敏感性分析的遗漏变量偏误框架AI 解读

Abstract We develop an omitted variable bias framework for sensitivity analysis of instrumental variable estimates that naturally handles multiple side effects (violations of the exclusion restriction assumption) and confounders (violations of the ignorability of the instrument assumption) of the instrument, exploits expert knowledge to bound sensitivity parameters and can be easily implemented with standard software. Specifically, we introduce sensitivity statistics for…

This Special Issue celebrates the seminal contributions of Philip G. Wright to causal inference in economics and puts them in a modern perspective. At its core is a reproduction of Online Appendix B in Wright’s 1928 book The Tariff on Animal and Vegetable Oils.1 This book deals with demand for and supply of oils and butter. In its Online Appendix B, Wright made several fundamental contributions to causal inference. He introduced a structural equation model of demand and…

关税对价格与产出的影响：以黄油和亚麻籽为重点AI 解读

Editorial statement: This is a reproduction of Appendix B in Wright, P. G. (1928). The Tariff on Animal and Vegetable Oils, 286–319. New York: The Macmillan Company. We have corrected four minor errors in its mathematical expressions: We have made further typographical changes to improve readability: We (i) have placed all footnotes, some of which are long and contain graphics, in a new section at the end; (ii) have reconstructed all graphics using TikZ, with some added…