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非参数工具变量回归中泛函的自适应估计AI 解读

We consider the problem of estimating the value ℓ ( ϕ ) of a linear functional, where the structural function ϕ models a nonparametric relationship in presence of instrumental variables. We propose a plug-in estimator which is based on a dimension reduction technique and additional thresholding. It is shown that this estimator is consistent and can attain the minimax optimal rate of convergence under additional regularity conditions. This, however, requires an optimal choice…

多个变量缺失情形下的广义矩估计AI 解读

We consider efficient estimation in moment conditions models with non-monotonically missing-at-random (MAR) variables. A version of MAR point-identifies the parameters of interest and gives a closed-form efficient influence function that can be used directly to obtain efficient semi-parametric generalized method of moments (GMM) estimators under standard regularity conditions. A small-scale Monte Carlo experiment with MAR instrumental variables demonstrates that the…

凸性约束下非参数工具变量回归的不适定性AI 解读

This note shows that adding monotonicity or convexity constraints on the regression function does not restore well‐posedness in nonparametric instrumental variable regression. The minimum distance problem without regularization is still locally ill‐posed.

多弱工具变量模型的自助法推断AI 解读

This study’s main contribution is to theoretically analyze the application of bootstrap methods to instrumental variable models when the available instruments may be weak and the number of instruments goes to infinity with the sample size. We demonstrate that a standard residual-based bootstrap procedure cannot consistently estimate the distribution of the limited information maximum likelihood estimator or Fuller (1977) estimator under many/many weak instrument sequence.…

股票收益可预测性的再考察AI 解读

We provide a simple and innovative approach to test for predictability in stock returns. Our approach consists of two methodologies, time change and instrumental variable estimation, which are employed respectively to deal effectively with persistent stochastic volatility in stock returns and endogenous nonstationarity in their predictors. These are prominent characteristics of the data used in predictive regressions, which are known to have a substantial impact on the test…

银行的地理扩张能否降低风险？AI 解读

We develop a new identification strategy to evaluate the impact of the geographic expansion of a bank holding company (BHC) across US metropolitan statistical areas (MSAs) on BHC risk. For the average BHC, the instrumental variable results suggest that geographic expansion materially reduces risk. Geographic diversification does not affect loan quality. The results are consistent with arguments that geographic expansion lowers risk by reducing exposure to idiosyncratic local…

司机请注意，附近有儿童：交通、污染与婴儿健康AI 解读

We investigate the effects of automobile congestion on ambient air pollution and local infant mortality rates using data from California spanning 2002 to 2007. Constructing instrumental variables (IV) using the relationship of traffic, weather conditions, and pollutants, we show that particulate matter, even at modern levels, has large marginal effects on weekly infant mortality rates, especially for premature or low birthweight infants. We also find suggestive evidence of…

通过辅助样本向研究样本倾斜法（AST）有效估计数据组合模型AI 解读

We propose a locally efficient estimator for a class of semiparametric data combination problems. A leading estimand in this class is the average treatment effect on the treated (ATT). Data combination problems are related to, but distinct from, the class of missing data problems with data missing at random (of which the average treatment effect (ATE) estimand is a special case). Our estimator also possesses a double robustness property. Our procedure may be used to…

In fuzzy regression discontinuity (FRD) designs, the treatment effect is identified through a discontinuity in the conditional probability of treatment assignment. We show that when identification is weak (i.e., when the discontinuity is of a small magnitude), the usual t-test based on the FRD estimator and its standard error suffers from asymptotic size distortions as in a standard instrumental variables setting. This problem can be especially severe in the FRD setting…

This article provides a strategy to identify the existence and direction of a causal effect in a generalized nonparametric and nonseparable model identified by instrumental variables. The causal effect concerns how the outcome depends on the endogenous treatment variable. The outcome variable, treatment variable, other explanatory variables, and the instrumental variable can be essentially any combination of continuous, discrete, or “other” variables. In particular, it is…

动态处理效应AI 解读

This paper develops robust models for estimating and interpreting treatment effects arising from both ordered and unordered multi-stage decision problems. Identification is secured through instrumental variables and/or conditional independence (matching) assumptions. We decompose treatment effects into direct effects and continuation values associated with moving to the next stage of a decision problem. Using our framework, we decompose the IV estimator, showing that IV…

押注低贝塔是否获利？贝塔异象的条件表现AI 解读

ABSTRACT Prior studies find that a strategy that buys high‐beta stocks and sells low‐beta stocks has a significantly negative unconditional capital asset pricing model (CAPM) alpha, such that it appears to pay to “bet against beta.” We show, however, that the conditional beta for the high‐minus‐low beta portfolio covaries negatively with the equity premium and positively with market volatility. As a result, the unconditional alpha is a downward‐biased estimate of the true…

函数系数模型的筛工具变量分位数回归估计AI 解读

In this paper we consider sieve instrumental variable quantile regression (IVQR) estimation of functional coefficient models where the coefficients of endogenous regressors are unknown functions of some exogenous covariates. We estimate the functional coefficients by the sieve-IVQR technique and establish the uniform consistency and asymptotic normality of the estimators. Based on the sieve estimates, we propose a nonparametric specification test for the constancy of the…

用于工具变量选择的Lasso：一项复现研究AI 解读

Summary Recently, Lasso methods have been applied to economic questions. In a seminal paper, Belloni et al . ( Econometrica ; 80 (6): 2369–2429) make use of (post‐)Lasso for instrumental variable selection in a setting where the number of instruments p is large or might even exceed the number of observations n —a situation which is prevalent in many current applications. We replicate their simulation study with the statistical package R (R Development Core Team ( )) and,…

若干具有内生性的随机前沿模型AI 解读

We consider mostly Bayesian estimation of stochastic frontier models where one-sided inefficiencies and/or the idiosyncratic error term are correlated with the regressors. We begin with a model where a Chamberlain–Mundlak device is used to relate a transformation of time-invariant effects to the regressors. This basic model is then extended in two directions: first an extra one-sided error term is added to allow for time-varying efficiencies. Second, a model with an equation…

效率前沿的非参数工具变量估计AI 解读

The paper investigates endogeneity issues in nonparametric frontier models. It considers a nonseparable model for a cost function C=φ(Y,U) where C and Y are the cost and the output, U is uniform in [0,1] and φ is increasing with respect to U. The cost frontier corresponds to U=0 and U can be interpreted as a normalized level of inefficiency. The endogeneity issue arises when Y is dependent of U. For identification and estimation, we use a nonparametric instrumental variables…

非参数前沿估计中的不可观测异质性与内生性AI 解读

In production theory, firm efficiencies are measured by their distances to a production frontier. In the presence of heterogeneous conditions (like environmental factors) that may influence the shape and the position of the frontier, traditional measures of efficiency obtained in the space of inputs/outputs are difficult to interpret, since they mix managerial inefficiency and shift of the frontier. This can be corrected by using nonparametric conditional efficiencies. In…

多内生变量线性工具变量模型的弱工具F检验AI 解读

We consider testing for weak instruments in a model with multiple endogenous variables. Unlike Stock and Yogo (2005), who considered a weak instruments problem where the rank of the matrix of reduced form parameters is near zero, here we consider a weak instruments problem of a near rank reduction of one in the matrix of reduced form parameters. For example, in a two-variable model, we consider weak instrument asymptotics of the form π1=δπ2+c/n where π1 and π2 are the…

数量随样本增长的矩条件估计量平均AI 解读

We establish the consistency and asymptotic normality for a class of estimators that are linear combinations of a set of $\sqrt n$ -consistent nonlinear estimators whose cardinality increases with sample size. The method can be compared with the usual approaches of combining the moment conditions (GMM) and combining the instruments (IV), and achieves similar objectives of aggregating the available information. One advantage of aggregating the estimators rather than the…

部分工具变量无效时的工具变量估计及孟德尔随机化应用AI 解读

Instrumental variables have been widely used for estimating the causal effect between exposure and outcome. Conventional estimation methods require complete knowledge about all the instruments’ validity; a valid instrument must not have a direct effect on the outcome and not be related to unmeasured confounders. Often, this is impractical as highlighted by Mendelian randomization studies where genetic markers are used as instruments and complete knowledge about instruments’…

医疗支出价格弹性的删失分位数工具变量估计AI 解读

Efforts to control medical care costs depend critically on how individuals respond to prices. I estimate the price elasticity of expenditure on medical care using a censored quantile instrumental variable (CQIV) estimator. CQIV allows estimates to vary across the conditional expenditure distribution, relaxes traditional censored model assumptions, and addresses endogeneity with an instrumental variable. My instrumental variable strategy uses a family member's injury to…

死亡截断问题中利用结局测量后的信息AI 解读

Summary Many clinical studies on non-mortality outcomes such as quality of life suffer from the problem that the non-mortality outcome can be censored by death, i.e. the non-mortality outcome cannot be measured if the subject dies before the time of measurement. To address the problem that this censoring by death is informative, it is of interest to consider the average effect of the treatment on the non-mortality outcome among subjects whose measurement would not be…

股票回购与流动性AI 解读

We analyze the impact of share repurchases on liquidity based on a new comprehensive data set of realized share repurchases in the US, which covers 50,204 repurchase months between 2004 and 2010. Using instrumental variable analysis, we show that repurchases unequivocally improve liquidity and suggest that endogenous controls have confounded results in earlier studies. Liquidity also influences how firms execute repurchase programs. Repurchases provide liquidity when other…

空间 Durbin 面板模型的识别AI 解读

Summary This paper considers identification of spatial Durbin dynamic panel models under 2SLS and ML estimations. We show that the parameters are generally identified via 2SLS moment relations or expected log‐likelihood or quasi‐likelihood functions. Monte Carlo experiments suggest that omitting relevant Durbin terms can result in significant biases in regression estimates, while including an irrelevant Durbin term causes no obvious loss of efficiency. Empirical illustration…