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可解释的市场微观结构噪声AI 解读

High-frequency financial data are often contaminated by market microstructure effects. In this study, we consider a setting where a portion of the microstructure noise can be explained by observable trading information, referred to as the explicative noise component. To formally analyze this component, we first develop a model-free variable importance measure in the high-frequency setting that quantifies the price impact of subsets of trading variables. Based on the…

广义单调性对平均处理效应的识别力AI 解读

In the context of a binary outcome, treatment, and instrument, Balke and Pearl establish that the monotonicity condition of Imbens and Angrist has no identifying power beyond instrument exogeneity for average potential outcomes and average treatment effects in the sense that adding it to instrument exogeneity does not decrease the identified sets for those parameters whenever those restrictions are consistent with the distribution of the observable data. This article shows…

控制错误发现率的局部关联搜索AI 解读

We introduce local conditional hypotheses that express how the relation between explanatory variables and outcomes changes across different contexts, described by covariates. By expanding upon the model-X knockoff filter, we show how to adaptively discover these local associations, all while controlling the false discovery rate. Our enhanced inferences can help explain sample heterogeneity and uncover interactions, making better use of the capabilities offered by modern…

一般混杂下线性非高斯因果模型的参数识别AI 解读

Linear non-Gaussian causal models postulate that each random variable is a linear function of parent variables and non-Gaussian exogenous error terms. We study identification of the linear coefficients when such models contain latent variables. Our focus is on the commonly studied acyclic setting, where each model corresponds to a directed acyclic graph (DAG). For this case, prior literature has demonstrated that connections to overcomplete independent component analysis…

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弱可识别条件下主成分分析的秩检验AI 解读

In a triangular array framework where n observations are randomly sampled from a p-dimensional elliptical distribution with shape matrix Vn, we consider the problem of testing the null hypothesis H0:θ=θ0 against the alternative hypothesis H1:θ≠θ0, where θ is the (fixed) leading unit eigenvector of Vn and θ0 is a given unit p-vector. The dependence of the shape matrix on the sample size allows us to consider challenging asymptotic scenarios in which the parameter of interest…

符号约束 VAR 模型中的常规冲击响应先验AI 解读

ABSTRACT Some studies have expressed concern that the Gaussian‐inverse Wishart–Haar prior typically employed in estimating sign‐identified VAR models may be unintentionally informative about the prior for the structural responses. We discuss what features to look for in this prior in the absence of specific prior information about the responses, building on the notion of weakly informative priors in Gelman et al. (2013), and in the presence of such information. Empirical…

理性预期下具有异质同伴效应的计数数据模型AI 解读

ABSTRACT This paper develops a peer effect model for count responses under rational expectations. The model accounts for heterogeneity in peer effects across groups based on observed characteristics. Identification is based on the linear model condition that requires the presence of friends of friends who are not direct friends. I show that this identification condition extends to a broad class of nonlinear models. Parameters are estimated using a nested pseudo‐likelihood…

资源错配下的生产函数估计AI 解读

ABSTRACT We show that the proxy variable method fails to yield consistent estimates of production function coefficients in the presence of resource misallocation. This failure arises because unobserved firm‐specific distortions violate the assumptions of scalar unobservability and strict monotonicity. We propose a novel identification strategy that does not rely on these assumptions. Our method is robust to various distortions and production function specifications, and can…

不完美的合成控制AI 解读

ABSTRACT The synthetic control method assumes the existence of a perfect synthetic control, which cannot exist if the outcomes are functions of transitory shocks with nonzero asymptotic variance and may not exist even in expectation for the treated unit. This paper first shows the benefits of estimating synthetic controls for all units. If the treated unit composes part of the synthetic control for any untreated unit, the treatment effect is independently identified by the…

无限阶面板自回归过程的工具变量估计AI 解读

We consider instrumental variables (IV) estimation of a possibly infinite order dynamic panel autoregressive (AR) process with individual effects. The estimation is based on the sieve AR approximation, with its lag order increasing with sample size. Transforming the variable to eliminate individual effects generates an endogeneity problem, particularly when the time series is only moderately long. IV approaches are useful to obtain well-behaved estimators in panels with…

异质网络潜在空间的高效分析AI 解读

This work proposes a unified framework for efficient estimation under latent space modeling of heterogeneous networks. We consider a class of latent space models that decompose latent vectors into shared and network-specific components across networks. We develop a novel procedure that first identifies the shared latent vectors and further refines estimates through efficient score equations to achieve statistical efficiency. Oracle error rates for estimating the shared and…

时空动态模型的结构识别AI 解读

Identifying latent cluster structures in spatial trends constitutes an important yet challenging task in diverse applications. In this paper, we propose a novel method based on a discrepancy measure over small spatial blocks that effectively uncovers heterogeneity within dynamic spatial data. Our approach effectively detects boundaries where structural changes occur, thus allowing for more nuanced insights into underlying spatial patterns. Unlike methods predicated on strong…

含未观测异质性的大型面板多重结构突变估计AI 解读

This article proposes a novel algorithm to identify potentially multiple breaks in linear panel data models, while the slope coefficients can have individual heterogeneity and the cross-sectional dimension is allowed to diverge jointly with the time span. The algorithm adopts a shrinkage penalty to detect breaks, and delivers a consistent estimator for the number of breaks and fraction-consistent estimators for break dates. In the presence of latent grouped heterogeneity, we…

光遗传学的非参数因果推断：动态策略的序贯偏离效应AI 解读

Optogenetics is a powerful neuroscience technique for studying how neural circuit manipulation affects behavior. Standard analysis conventions discard information and severely limit the scope of the causal questions that can be probed. To address this gap, we 1) draw connections to the causal inference literature on sequentially randomized experiments, 2) propose a non-parametric framework for analyzing "open-loop" (static regime) optogenetics behavioral experiments, 3)…

重新审视时间不一致偏好的非参数分析AI 解读

Abstract We revisit the recent revealed preference analysis of sophisticated quasi-hyperbolic consumers by Blow et al. [(2021), “Non-parametric Analysis of Time-Inconsistent Preferences”, The Review of Economic Studies, 88, 2687–2734] (BBC). We show that BBC’s revealed preference test is too lax. There are non-rationalizable data that would pass their test. A basic problem with their test is that it requires finding a certain endogenous elasticity, without regard to the…

时间不一致模型的识别：以杀虫蚊帐为例AI 解读

Abstract Time-inconsistency may play a central role in explaining inter-temporal behaviour, particularly among poor households. However, little is known about the distribution of time-inconsistent agents, and time-preference parameters are typically not identified in standard dynamic choice models. We formulate a dynamic discrete choice model in an unobservedly heterogeneous population of possibly time-inconsistent agents. We provide conditions under which all population…

动态选择下实验与观察数据结合的信息价值AI 解读

The Average Treatment Effect on the Treated Survivors (ATETS; Vikström et al., 2018) captures a composite effect of time-varying treatment and dynamic selection into the survivor population. We address the problem of identifying this treatment-effect parameter in the absence of long-term experimental data, utilizing available long-term observational data instead. This poses a nontrivial challenge in practice, as dynamic selection compounds static selection in observational…

积分波动率与瞬时波动率的稳健估计AI 解读

We introduce a new method to estimate the integrated volatility (IV) and the spot volatility (SV) based on noisy high-frequency data. Our method employs the ReMeDI approach introduced by Li and Linton (2022) to estimate the moments of microstructure noise and thereby eliminate their influence, and the pre-averaging method to target the volatility parameter. The method is robust: it can be applied when the efficient price exhibits stochastic volatility and jumps, the…

通过一阶条件揭示工厂—产品生产率：对 Orr（2022）的稳健复现AI 解读

ABSTRACT We assess the replicability of Orr's (2022) method for estimating within‐plant productivity across product lines, which combines demand estimation with cost minimization. The original study uses input price shocks in other output markets as instrumental variables, with exclusion restrictions based on downstream purchase shares. Reconstructing the original dataset of Indian machinery producers from 2000–2007, we reproduce the main productivity patterns and…

基于广义特征值的高维稀疏回归方向识别与极小极大估计AI 解读

In high-dimensional (HD) sparse linear regression, parameter selection and estimation are addressed using a constraint $l_0$ on the direction of the parameter vector. We begin by establishing a general result that identifies this direction through the leading generalized eigenspace of specific measurable matrices. Using this result, we propose a novel approach to the selection of the best subsets by solving an empirical generalized eigenvalue problem to estimate the…

利用可能相依冲击的非高斯性识别结构向量自回归AI 解读

Summary We complement previous partial global identification results for the non-Gaussian structural vector autoregressive (SVAR) model by showing that in the absence of co-skewness among the strucural shocks, the skewed shocks are identified and in the absence of excess co-kurtosis, the shocks with non-zero excess kurtosis are identified. The former case has the advantage that dependent conditional heteroskedasticity is allowed for. In each case, the remaining shocks are…

部分识别下处理选择问题的决策理论AI 解读

Abstract We apply classical statistical decision theory to a large class of treatment choice problems with partial identification. We show that, in a general class of problems with Gaussian likelihood, all decision rules are admissible; it is maximin-welfare optimal to ignore all data; and, for severe enough partial identification, there are infinitely many minimax-regret optimal decision rules, all of which sometimes randomize the policy recommendation. We uniquely…

节点归属随时间变化的异配随机块模型动态聚类AI 解读

Summary We consider a time-ordered sequence of networks stemming from stochastic block models in which nodes gradually change their membership over time, and no network at any single time-point contains sufficient signal strength to recover its community structure. To estimate the time-varying community structure, we develop the kernel-debiased sum of squares method that performs spectral clustering after a debiased sum-of-squared aggregation of adjacency matrices. Our…

多处理与正值性违背下的因果参数比较AI 解读

Summary Comparing outcomes across treatments is essential in medicine and public policy. To do so, researchers typically estimate a set of parameters, possibly counterfactual, each targeting adifferent treatment. Treatment-specific means are commonly used, but their identification requires a positivity assumption: every subject has a nonzero probability of receiving each treatment. This assumption is often implausible, especially when treatment can take many values. Causal…