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动态面板处理效应的双重／去偏机器学习AI 解读

We introduce a novel framework for causal inference in dynamic panel data that extends the cross-sectional Double/Debiased Machine Learning (DML) approach. The method rests on a partially linear dynamic panel model with two-way fixed effects for potential outcomes and readily accommodates binary, multi-valued, or continuous treatments. To translate potential outcome models into an estimable form, we introduce dynamic conditional independence assumptions. By integrating…

多重实验的潜力：识别潜在结果的联合分布AI 解读

Abstract Typical causal effects are defined based on the marginal distribution of potential outcomes. However, many real-world applications require causal estimands involving the joint distribution of potential outcomes to enable nuanced treatment evaluation and selection. In this article, we propose a novel framework for identifying and estimating the joint distribution of potential outcomes using multiple experimental datasets. We introduce the assumption of…

面板双重差分估计的简单变换方法AI 解读

In the case of panel data with staggered interventions, we propose a simple time-series transformation that can be combined with various treatment effect estimators. The transformation is at the unit level, and simply requires computing the average outcome prior to an intervention, subtracting it from a post-treatment outcome, and then carefully selecting the control units in each time period. We show that under no anticipation and parallel trends assumptions, the cohort…

协变量分类差异下的预测：以医疗风险评估为例AI 解读

A common practice in evidence-based decision-making uses estimates of conditional probabilities P(y|x) obtained from research studies to predict outcomes y based on observed covariates x. Given this information, decisions are then based on the predicted outcomes. Researchers commonly assume that the predictors used in the generation of the evidence are the same as those used in applying the evidence: i.e., the meaning of x in the two circumstances is the same. This may not…

利用高阶多元累积量估计弱因子AI 解读

When factors are weak, covariance-based factor analysis methods tend to exhibit poor performance. To address this issue in the case of non-Gaussian data, we propose a new method called Higher-order multi-cumulant Factor Analysis (HFA). HFA estimates factors and factor loadings via the eigenvalue decomposition of the product of a higher-order multi-cumulant matrix and its transpose. We derive the asymptotic properties of HFA under a weak factor model where non-Gaussianity…

部分时期回归量完全缺失的非参数时变面板模型识别与估计AI 解读

This article considers a nonparametric time-varying panel data model in which the primary regressor of interest is completely missing in some periods. Under standard assumptions, identification results are established for the unknown functions and the distributions of the missing regressor. Specifically, one period of completely observed data suffices to identify the random effects model, whereas two periods are required for the fixed effects model. Following identification,…

高维环境中的涵盖检验与递进检验AI 解读

This paper introduces the Subseries-based Cauchy Combination Test (SCT), a novel procedure for testing encompassing relationships or model validity using identification conditions formulated as multiple moment restrictions. SCT applies to weakly or short-range-dependent data and eliminates the need to estimate high-dimensional covariance matrices. Unlike Wald- or J-type tests, it remains reliable in both low- and high-dimensional settings. The test is asymptotically unbiased…

区间识别集参数推断的功效性质AI 解读

This article studies the power properties of confidence intervals (CIs) for a partially-identified parameter of interest with an interval identified set. We assume that the researcher has bounds estimators needed to construct the CIs proposed by Imbens and Manski (2004), Stoye (2009), and Stoye (2020), denoted by C I α 1 $CI_{\alpha }^{1}$ upper C upper I Subscript alpha Superscript 1 , C I α 2 $CI_{\alpha }^{2}$ upper C upper I Subscript alpha Superscript 2 , C I α 3 $CI_{\alpha }^{3}$…

全球需求系统中的汇率与资产价格AI 解读

We develop an asset demand system to analyze the equilibrium relation between international portfolio holdings and flows, exchange rates, and asset prices across all countries. We introduce a nested logit model of asset demand, for which we develop a new identification strategy by instrumental variables. Averaged across years and issuer countries, the demand elasticities are 27.9 for short-term debt, 3.2 for long-term debt, and 1.2 for equity. These demand elasticities are…

观察性研究中因果推断的样本量与检验力计算AI 解读

This paper investigates the theoretical foundation and develops analytical formulas for sample size and power calculations for causal inference with observational data. By analyzing the variance of an inverse probability weighting estimator of the average treatment effect, we decompose the power calculation into three components: propensity score distribution, potential outcome distribution and their correlation. We show that to determine the minimal sample size of an…

以可检验假设扩展经济模型：理论与应用AI 解读

This article studies the identification of complete economic models with testable assumptions. We start with a local average treatment effect ( L A T E $LATE$ upper L upper A upper T upper E ) model where the “No Defiers,” the independent IV assumption, and the exclusion restrictions can be jointly refuted by some data distributions. We propose two relaxed assumptions that are not refutable, with one assumption focusing on relaxing the “No Defiers” assumption while the other…

因果工具变量模型中的贝叶斯模型平均AI 解读

ABSTRACT Instrumental variables are a popular tool to infer causal effects under unobserved confounding, but choosing suitable instruments is challenging in practice. We propose gIVBMA, a Bayesian model averaging procedure that addresses this challenge by averaging across different sets of instrumental variables and covariates in a structural equation model. This allows for data‐driven selection of valid and relevant instruments and provides additional robustness against…

回归不连续效应及其外部有效性的联合推断AI 解读

ABSTRACT The external validity of regression discontinuity designs is crucial for informing policy but is rarely examined in applied work. To advance empirical practice, we propose a joint inference procedure for the treatment effect and its local external validity, captured by the treatment effect derivative (TED), within a robust bias correction framework. We further introduce a locally linear treatment effects assumption, which extends the scope of the TED and enables…

可靠性随时间变化的高频工具变量：理解宏观经济识别AI 解读

ABSTRACT The effects of monetary policy shocks are regularly estimated using high‐frequency surprises in asset prices around central bank meetings as an instrument. These studies, insofar as they explicitly model the relationship between instrument and structural shock, assume a constant relationship between the instrument and the monetary policy shock. By allowing for time variation in this relationship, we show that only a few distinct periods are informative about…

政党如何推动美国政治极化：1927—2018AI 解读

The current polarization of elites in the U.S., particularly in Congress, is frequently ascribed to the emergence of cohorts of ideologically extreme legislators replacing moderate ones.Politicians, however, do not operate as isolated agents, driven solely by their preferences.They act within organized parties, whose leaders exert control over the rank-and-file, directing support for and against policies.This paper shows that the omission of party discipline as a driver of…

通过稀疏性识别因子扩展 VAR 模型AI 解读

This paper introduces a regularized factor-augmented vector autoregressive (RFAVAR) model which incorporates sparsity in the factor loadings. Within this framework, the factors can load on a subset of variables, thereby enabling factor identification and enhancing their economic interpretation. The proposed RFAVAR model allows to investigate the effects of structural shocks on economically interpretable factors and on all observed time series included in the model. We prove…

自回归逆Wishart多元随机波动模型及其因子扩展AI 解读

ABSTRACT Despite its conceptual appeal, the autoregressive inverse‐Wishart (AIW) multivariate stochastic volatility model has been hindered by inefficient sampling methods. The existing samplers for the latent covariance matrix severely suffer from the curse of dimensionality and only work when the dimension is very low. In this paper, we introduce a new proposal for the latent covariance matrix in the AIW model, which demonstrates far better scalability. We introduce…

宏观审慎政策的尾部传导：叙事识别证据AI 解读

ABSTRACT We estimate the causal effects of macroprudential policies on the entire distribution of GDP growth for advanced European economies using a narrative‐identification strategy in a quantile‐regression framework. While macroprudential policy has near‐zero effects on the center of the GDP‐growth distribution, tighter policy brings benefits by reducing the variance of future growth, significantly boosting the left tail while simultaneously reducing the right. Assessing a…

树结构大规模稀疏线性方程组及其在光纤网络中的应用AI 解读

Fiber optic networks require precise time synchronization, yet asymmetric edges introduce delays that challenge accurate correction. Current solutions like manual detection or the deployment of Global Navigation Satellite System (GNSS) receivers are impractical due to high costs and labor requirements. Statistically, delay detection is complicated by non-identifiability, fixed tree topologies, and absent structured sparsity assumptions. To overcome these difficulties, we…

中介分析中复合原假设的最优检验AI 解读

The indirect effect of an exposure on an outcome through an intermediate variable can be identified by a product of two regression coefficients under certain causal and regression modeling assumptions. In this context, the null hypothesis of no indirect effect is a composite null hypothesis, as the null holds if either regression coefficient is zero. A consequence is that traditional hypothesis tests are severely underpowered near the origin (i.e., when both coefficients are…

生成式人工智能辅助因果推断：以文本作为处理AI 解读

In this paper, we demonstrate how to enhance the validity of causal inference with unstructured high-dimensional treatments like texts, by leveraging the power of generative Artificial Intelligence (GenAI). Specifically, we propose to use a deep generative model such as large language models (LLMs) to efficiently generate treatments and use their internal representation for subsequent causal effect estimation. We show that the knowledge of this true internal representation…

广义潜在因子模型的可识别性与推断AI 解读

Generalized latent factor analysis not only provides a useful latent embedding approach in statistics and machine learning, but also serves as a widely used tool across various scientific fields, such as psychometrics, econometrics, and social sciences. Ensuring the identifiability of latent factors and the loading matrix is essential for the model’s estimability and interpretability, and various identifiability conditions have been employed by practitioners. However,…

高维时间序列因子模型的权重校准估计AI 解读

The factor modeling for high-dimensional time series is powerful in discovering latent common components for dimension reduction and information extraction. Most available estimation methods can be divided into two categories: the covariance-based under asymptotically-identifiable assumption and the autocovariance-based with white idiosyncratic noise. This paper follows the autocovariance-based framework and develops a novel weight-calibrated method to improve the estimation…

部分识别下的稳健贝叶斯处理选择AI 解读

We study a class of binary treatment choice problems with partial identification through the lens of robust (multiple prior) Bayesian analysis. We use a convenient set of prior distributions to derive ex-ante and ex-post robust Bayes decision rules, both for decision makers who can randomize and for decision makers who cannot. Our main messages are as follows: First, ex-ante and ex-post robust Bayes decision rules do not agree in general, whether or not randomized rules are…