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竞争市场中的信息价值：来自中小企业的证据AI 解读

We empirically investigate how the performance of small and medium-sized enterprises (SMEs) changes when gaining access to market information. To do so, we evaluate the impact of an information program diffused by a bank among its SME customers. Adopting firms gained access to reports with rich information about their own clientele and that of nearby establishments. While we find that adoption is associated with a 4.5% revenue increase, our instrumental variable results…

作为弱工具变量检验的稳健 F 统计量AI 解读

For the linear model with a single endogenous variable, (Montiel Olea and Pflueger 2013) proposed the effective F-statistic as a test for weak instruments in terms of the Nagar bias of the two-stage least squares (2SLS) or limited information maximum likelihood (LIML) estimator relative to a benchmark worst-case bias. We show that their methodology for the 2SLS estimator applies to a class of linear generalized method of moments (GMM) estimators with an associated class of…

随机系数 Logit 需求模型估计中的模拟误差与数值不稳定AI 解读

The nonlinear GMM-IV estimator of Berry, Levinsohn and Pakes (1995) can suffer from numerical instability resulting in a wide range of parameter estimates and economic implications. This has been reported to depend on technical details such as the choice of the optimization algorithm, starting values, and convergence criteria. We show that numerical approximation errors in the estimator’s moment function are the main driver of this instability. With accurate approximation,…

连续内生变量与离散工具变量下非参数回归的迭代估计AI 解读

We consider a nonparametric regression model with continuous endogenous independent variables when only discrete instruments are available that are independent of the error term. Although this framework is very relevant for applied research, its implementation is challenging, as the regression function becomes the solution to a nonlinear integral equation. We propose a simple iterative procedure to estimate such models and showcase some of its asymptotic properties. In a…

协整向量自回归过程的统一推断AI 解读

Uniformly valid inference for cointegrated vector autoregressive processes has so far proven difficult due to certain discontinuities arising in the asymptotic distribution of the least squares estimator. We extend asymptotic results from the univariate case to multiple dimensions and show how inference can be based on these results. Furthermore, we show that lag augmentation and a recent instrumental variable procedure can also yield uniformly valid tests and confidence…

个体福利分析：随机拟线性效用、独立性与置信界AI 解读

We introduce a novel framework for individual-level welfare analysis. It builds on a parametric model for continuous demand with a quasilinear utility function, allowing for heterogeneous coefficients and unobserved individual-good-level preference shocks. We obtain bounds on the individual-level consumer welfare loss at any confidence level due to a hypothetical price increase, solving a scalable optimization problem constrained by a novel confidence set under an…

脉冲响应的均匀先验AI 解读

There has been a call for caution regarding the standard procedure for Bayesian inference in set‐identified structural vector autoregressions on the grounds that the common practice of using a uniform prior over the set of orthogonal matrices induces a non‐uniform prior for individual impulse responses or other quantities of interest. This paper challenges this call by formally showing that when the focus is on joint inference, the uniform prior over the set of orthogonal…

候选人综合素质的估计AI 解读

We estimate valence measures of candidates running in U.S. House elections from data on vote shares. Our identification and estimation strategy builds on ideas developed for estimating production functions, allowing us to control for possible endogeneity of campaign spending and sample selection of candidates due to endogenous entry. We find that incumbents have substantially higher valence measures than challengers running against them, resulting in about 3.5…

包含新处理方案的政策学习AI 解读

I study the problem of a decision maker choosing a policy that allocates treatment to a heterogeneous population on the basis of experimental data that includes only a subset of possible treatment values. The effects of new treatments are partially identified by shape restrictions on treatment response. Policies are compared according to the minimax regret criterion, and I show that the empirical analog of the population decision problem has a tractable linear‐ and…

随机系数潜在效用模型的识别AI 解读

This paper provides nonparametric identification results for random coefficient distributions in perturbed utility models. We cover discrete choice and models of multiple purchases. We establish identification using variation in mean quantities. The results apply even when an analyst observes only aggregate demands but not whether goods are chosen together. The identification results use exclusion restrictions on covariates and independence between random slope coefficients…

日内波动率曲线均值平稳性的检验AI 解读

We develop a test for mean stationarity of latent volatility curves using high‐frequency data. To derive the asymptotic test size and power, we establish a functional invariance principle for semimartingales under a strong mixing condition. The power properties are analyzed under alternatives featuring deterministic trends in the volatility curve dynamics. Application to S&P 500 futures data provides strong evidence of nonstationary variation in the volatility pattern, with…

连续更新 GMM 的双重稳健推断AI 解读

We propose the double robust Lagrange multiplier (DRLM) statistic for testing hypotheses specified on the minimizer of the population continuous updating objective function. The (bounding) χ 2 limiting distribution of the DRLM statistic is robust to both misspecification and weak identification, hence its name. The minimizer is the so‐called pseudo‐true value, which equals the true value of the structural parameter under correct specification. To emphasize its importance for…

多维类型保险模型的计量分析AI 解读

In this paper, we address the identification and estimation of insurance models where insurees have private information about their risk and risk aversion. The model includes random damages and allows for several claims, while insurees choose from a finite number of coverages. We show that the joint distribution of risk and risk aversion is nonparametrically identified despite bunching due to multidimensional types and a finite number of coverages. Our identification…

具有灵活加性结构与未知连接函数的广义加性模型：非参数识别与估计AI 解读

This paper proposes a nonparametric approach to identify and estimate the generalized additive model with a flexible additive structure and with possibly discrete variables when the link function is unknown. Our approach allows for a flexible additive structure which provides applied researchers the flexibility to specify their model according to economic theory or practical experience. Motivated by the concerns from empirical research, our method also allows for multiple…

序数结局下观察研究与不完全随机试验中获益指标的尖锐符号非参数界AI 解读

Abstract The probability of benefit can be a valuable and meaningful measure of treatment effect. Particularly for an ordinal outcome, it can have an intuitive interpretation. Unfortunately, this measure, and variations of it, are not identifiable even in randomized trials with perfect compliance. There is, for this reason, a long literature on nonparametric bounds for unidentifiable measures of benefit. These have primarily focused on perfect randomized trial settings and…

算法辅助人类决策的最优策略AI 解读

Summary We consider optimal regimes for algorithm-assisted human decision-making. Such regimes are decision functions of measured pre-treatment variables and, by leveraging natural treatment values, enjoy a superoptimality property whereby they are guaranteed to outperform conventional optimal regimes. When there is unmeasured confounding, the benefit of using superoptimal regimes can be considerable. When there is no unmeasured confounding, superoptimal regimes are…

非独立成分分析AI 解读

A seminal result in the ICA literature states that for AY=ε, if the components of ε are independent and at most one is Gaussian, then A is identified up to sign and permutation of its rows (Signal Process. 36 (1994)). In this paper we study to which extent the independence assumption can be relaxed by replacing it with restrictions on higher order moment or cumulant tensors of ε. We document new conditions that establish identification for several nonindependent component…

预测回归的新稳健推断方法AI 解读

We propose a robust inference method for predictive regression models under heterogeneously persistent volatility as well as endogeneity, persistence, or heavy-tailedness of regressors. This approach relies on two methodologies, nonlinear instrumental variable estimation and volatility correction, which are used to deal with the aforementioned characteristics of regressors and volatility, respectively. Our method is simple to implement and is applicable both in the case of…

非线性选择模型中的截距估计AI 解读

We propose various semiparametric estimators for nonlinear selection models, where slope and intercept can be separately identified. When the selection equation satisfies a monotonic index restriction, we suggest a local polynomial estimator, using only observations for which the marginal cumulative distribution function of the instrument index is close to one. Data-driven procedures such as cross-validation may be used to select the bandwidth for this estimator. We then…

Larsson、Ramdas 与 Ruf 对《安全检验》的讨论AI 解读

We congratulate the authors on a stimulating article that has inspired many follow-up works. We discuss the central concept of growth-rate optimality (GRO). For a point null and alternative, the GRO e-variable is their likelihood ratio (Shafer, 2021). The generalization to composite nulls takes centre stage in Grünwald et al. (2024). Their use of the reverse information projection (RIPr) builds on Li (1999), and has been recently extended in Lardy et al. (2023). Inspired by…

处理依赖性右删失的近端生存分析AI 解读

Abstract Many epidemiological and clinical studies aim to analyse a time-to-event endpoint. A common complication is right censoring. In some cases, right censoring occurs when subjects are still surviving after the study terminates or move out of the study area. In such cases, right censoring is typically treated as independent or noninformative. This assumption can be further relaxed to conditional independent censoring by leveraging possibly time-varying covariate…

大量混合工具变量与控制变量下的内生处理效应估计AI 解读

Abstract Instrumental variables (IVs) and control variables are frequently used to assist researchers in investigating endogenous treatment effects. When used together, their identities are typically assumed to be known. However, in many practical situations, one is faced with a large and mixed set of covariates, some of which can serve as excluded IVs, some can serve as control variables, whereas others should be discarded from the model. It is often not possible to…

高维时间序列的一致因果推断AI 解读

A methodology for high-dimensional causal inference in a time series context is introduced. Time series dynamics are captured by a Gaussian copula, and estimation of the marginal distribution of the data is not required. The procedure can consistently identify the parameters that describe the dynamics of the process and the conditional causal relations among the possibly high-dimensional variables, under sparsity conditions. Identification of the causal relations is in the…

用凝聚型层次聚类选择有效工具变量AI 解读

Summary We propose a procedure that combines hierarchical clustering with a test of overidentifying restrictions for selecting valid instrumental variables (IV) from a large set of IVs. Some of these IVs may be invalid in that they fail the exclusion restriction. We show that if the largest group of IVs is valid, our method achieves oracle properties. Unlike existing techniques, our work deals with multiple endogenous regressors. Simulation results suggest an advantageous…