论文检索

非中心 Wishart 矩阵逆的性质AI 解读

The inverse of a noncentral Wishart matrix occurs in a variety of contexts in multivariate statistical work, including instrumental variable (IV) regression, but there has been very little work on its properties. In this paper, we first provide an expression for the expectation of the inverse of a noncentral Wishart matrix, and then go on to do the same for a number of scalar-valued functions of the inverse. The main result is obtained by exploiting simple but powerful…

对《叙事限制与代理变量》的讨论AI 解读

I am grateful for the chance to discuss this characteristically insightful paper by Giacomini, Kitagawa, and Read (hence-forth GKR). Since the seminal contribution of Antolín-Díaz and Rubio-Ramírez (2018), narrative restrictions have rapidly become one of the go-to tools for sharpening causal inference in SVAR analysis. Giacomini, Kitagawa, and Read (2021) con-tributed greatly to our understanding of the role of subjective prior beliefs and the appropriate form of the…

工具变量下局部平均处理效应的高维模型辅助推断AI 解读

Consider the problem of estimating the local average treatment effect with an instrument variable, where the instrument unconfoundedness holds after adjusting for a set of measured covariates. Several unknown functions of the covariates need to be estimated through regression models, such as instrument propensity score and treatment and outcome regression models. We develop a computationally tractable method in high-dimensional settings where the numbers of regression terms…

局部平均处理效应的高效协变量平衡AI 解读

This article develops an empirical balancing approach for the estimation of treatment effects under two-sided noncompliance using a binary instrumental variable. The method weighs both treatment and outcome information with inverse probabilities to impose exact finite sample balance across instrument level groups. It is free of functional form assumptions on the outcome or the treatment selection step. By tailoring the loss function for the instrument propensity scores, the…

高维混频工具变量回归AI 解读

This article introduces a high-dimensional linear IV regression for the data sampled at mixed frequencies. We show that the high-dimensional slope parameter of a high-frequency covariate can be identified and accurately estimated leveraging on a low-frequency instrumental variable. The distinguishing feature of the model is that it allows handing high-dimensional datasets without imposing the approximate sparsity restrictions. We propose a Tikhonov-regularized estimator and…

对潜在非平稳性稳健的结构 VAR 工具变量估计AI 解读

This paper considers the estimation of dynamic causal effects using a proxy structural vector-autoregressive model with possibly nonstationary regressors. We provide general conditions under which the asymptotic normal approximation remains valid. In this case, the asymptotic variance depends on the persistence property of each series. We further provide a consistent asymptotic covariance matrix estimator that requires neither knowledge of the presistence properties of the…

众多弱工具变量下的统计推断AI 解读

Abstract We develop a concept of weak identification in linear instrumental variable models in which the number of instruments can grow at the same rate or slower than the sample size. We propose a jackknifed version of the classical weak identification-robust Anderson–Rubin (AR) test statistic. Large-sample inference based on the jackknifed AR is valid under heteroscedasticity and weak identification. The feasible version of this statistic uses a novel variance estimator.…

具有有限混合结构的计数 Roy 模型AI 解读

Summary This paper develops the Finite Mixture Roy model for count variables and uses this semiparametric model to analyze the effect of supplemental Medigap private insurance on the demand for prescription drugs for the U.S. elderly unemployed Medicare population. The model is an extension of the Count Roy model, which produces unrealistic treatment effects when observed count patterns are consistent with finite mixtures. To estimate the numbers of components in the…

含双向固定效应的指数回归工具变量估计及引力方程应用AI 解读

Summary This paper introduces instrumental‐variable estimators for exponential‐regression models that feature two‐way fixed effects. These techniques allow us to develop a theory‐consistent approach to the estimation of cross‐sectional gravity equations that can accommodate the endogeneity of policy variables. We apply this approach to a dataset in which the policy decision of interest is the engagement in a free‐trade agreement. We explore ways to exploit the transitivity…

动态方差分解的工具变量识别AI 解读

Macroeconomists increasingly use external sources of exogenous variation for causal inference. However, unless such external instruments (proxies) capture the underlying shock without measurement error, existing methods are silent on the importance of that shock for macroeconomic fluctuations. We show that, in a general moving-average model with external instruments, variance decompositions for the instrumented shock are interval-identified, with informative bounds. Various…

含非参数内生效应的横截面互动模型之筛工具变量估计AI 解读

In this study, we consider cross-sectional interaction models including spatial autoregressive models and peer effects models as special cases. Our model allows the endogenous effect – the effect of others’ outcomes on one’s own outcome – to be nonlinear and nonparametric. For the model estimation, we propose a sieve instrumental variable estimator and establish both its consistency and asymptotic normality. Furthermore, we propose a nonparametric specification test for the…

右删失持续时间结果的非参数工具变量回归AI 解读

This article analyzes the effect of a discrete treatment Z on a duration T. The treatment is not randomly assigned. The confounding issue is treated using a discrete instrumental variable explaining the treatment and independent of the error term of the model. Our framework is nonparametric and allows for random right censoring. This specification generates a nonlinear inverse problem and the average treatment effect is derived from its solution. We provide local and global…

Choi 与 Wong 对《广义线性模型参数的弱假设推断》的讨论AI 解读

We congratulate the authors for major advances in the methodology of estimands, in particular assumption-lean reference for the estimands in possibly misspecified generalised linear models. Whereas the simulation studies in Section 6 indeed support the ‘assumption-lean’ claim and the authors’ software in Practice of Epidemiology187 (pp. 1079–1084, 2018) facilitates such inference, the empirical example in Section 7 seems problematic to illustrate applications of the…

多因子误差结构下异质空间面板的估计与推断AI 解读

We develop a unifying econometric framework for the analysis of heterogeneous panel data models that can account for both spatial dependence and common factors. To tackle the challenging issues of endogeneity due to the spatial lagged term and the correlation between the regressors and factors, we propose the CCEX-IV estimation procedure that approximates factors by the cross-section averages of regressors and deals with the spatial endogeneity using the internal…

多资产因子模型：强因子、弱因子与两步估计AI 解读

This paper re-examines the problem of estimating risk premia in unconditional linear factor pricing models. Typically, the data used in the empirical literature are characterized by weakness of some pricing factors, strong cross-sectional dependence in the errors, and (moderately) high cross-sectional dimensionality. Using an asymptotic framework where the number of assets/portfolios grows with the time span of the data while the risk exposures of weak factors are…

利用多个可能无效的工具变量构造证据因子AI 解读

Valid instrumental variables enable treatment effect inference even when selection into treatment is biased by unobserved confounders. When multiple candidate instruments are available, but some of them are possibly invalid, the previously proposed reinforced design enables one or more nearly independent valid analyses that depend on very different assumptions. That is, we can perform evidence factor analysis. However, the validity of the reinforced design depends crucially…

信号、工具变量与首席财务官决策AI 解读

Building parable economies embedding econometricians, we view alternative estimators (Instrumental variables, fuzzy regression discontinuity, natural experiments, OLS, event studies) from the perspective of privately informed decision-makers, e.g., CFOs. Instrumental variable estimates can be misleading since randomization through observable instruments eliminates signal content arising from discretion. If the goal is informing discretionary decisions, rather than predicting…

早年饥荒暴露、饥饿回忆与晚年健康AI 解读

Abstract We use newly collected individual‐level hunger recall information from the China Family Panel Survey to estimate the causal effect of undernourishment on later‐life health. We develop a two‐sample instrumental variable (TSIV) estimator that can deal with heterogeneous samples. We find a nonlinear relationship between mortality rates, a commonly used famine indicator, and the individual hunger experience. The nonlinearity in famine exposure may explain the variation…

重新评估瑞典综合学校改革：教育与收入效应AI 解读

Summary We revisit a Swedish comprehensive school reform first evaluated by Meghir and Palme (2005). This reform increased years of schooling and abolished tracking. We extend the original analysis to the full population and introduce an improved education measure. Our results confirm the original overall finding of small average earnings effects. However, we find considerably larger increases in educational attainment and no evidence of decreased labor earnings for students…

工具变量不完美时处理效应的非参数界限AI 解读

Summary This paper extends the identification results in Nevo and Rosen (2012) to nonparametric models. We derive nonparametric bounds on the average treatment effect when an imperfect instrument is available. As in Nevo and Rosen (2012), we assume that the correlation between the imperfect instrument and the unobserved latent variables has the same sign as the correlation between the endogenous variable and the latent variables. We show that the monotone treatment selection…

含交互效应的线性面板模型两阶段工具变量估计AI 解读

Summary This paper analyses the instrumental variables (IV) approach put forward by Norkute et al. (2021), in the context of static linear panel data models with interactive effects present in the error term and the regressors. Instruments are obtained from transformed regressors, thereby it is not necessary to search for external instruments. We consider a two-stage IV (2SIV) and a mean-group IV (MGIV) estimator for homogeneous and heterogeneous slope models, respectively.…

利用经验似然检验条件矩限制模型AI 解读

Summary An empirical likelihood test is proposed for parameters of models defined by conditional moment restrictions, such as models with nonlinear endogenous covariates, with and without heteroscedastic errors and non-separable transformation models. The number of empirical likelihood constraints is given by the size of the parameter, unlike alternative semi-parametric approaches. We show that the empirical likelihood ratio test is asymptotically pivotal, without explicit…

K类估计量与PULSE的分布稳健性AI 解读

Summary While causal models are robust in that they are prediction optimal under arbitrarily strong interventions, they may not be optimal when the interventions are bounded. We prove that the classical K-class estimator satisfies such optimality by establishing a connection between K-class estimators and anchor regression. This connection further motivates a novel estimator in instrumental variable settings that minimizes the mean squared prediction error subject to the…

非光滑备择下的最优极小极大速率AI 解读

Summary This study investigates optimal minimax rates for specification testing when the alternative hypothesis is built on a set of nonsmooth functions. The set consists of bounded functions that are not necessarily differentiable with no smoothness constraints imposed on their derivatives. In the instrumental variable regression set up with an unknown error variance structure, we find that the optimal minimax rate is $n^{-1/4}$, where n is the sample size. The rate is…