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结合统计识别与潜在内生代理变量估计财政乘数AI 解读

Summary Different proxy variables used in fiscal policy structural vector autoregressions (SVARs) lead to contradicting conclusions regarding the size of fiscal multipliers. Our analysis suggests that the conflicting results may stem from violations of the proxy exogeneity assumptions. We propose a novel approach to include proxy variables in a Bayesian non-Gaussian SVAR, tailored to accommodate potentially endogenous proxies. Using our model, we find that increasing…

工具变量单调性的识别内容AI 解读

SUMMARY: The existing literature has established that the instrument monotonicity assumption of Imbens and Angrist (1994) has no identifying content on the average treatment effect and more generally on the marginal distributions of potential outcomes in the sense that additionally imposing this assumption does not affect the identified set for the parameters of interest under instrument exogeneity whenever these assumptions are consistent with the data. This paper…

SUMMARY This paper extends difference-in-differences (DiD) to settings with continuous treatments. Specifically, the average treatment effect on the treated (ATT) at any level of treatment intensity is identified under a conditional parallel trends assumption. Estimating the ATT in this framework requires first estimating infinite-dimensional nuisance parameters, particularly the conditional density of the continuous treatment, which can introduce substantial bias. To…

资本市场的繁荣、萧条与错配：来自海上油气行业的证据AI 解读

How efficiently do markets reallocate capital in booms and busts? Using a novel dataset of offshore drilling contracts, I examine the role of matching in shaping industry reallocation. Oil companies search and match with capital (rigs) in a decentralized market. I find that oil and gas booms increase the option value of searching, which leads agents to avoid bad matches, reducing mismatch through a sorting effect. I provide an identification strategy to disentangle…

内生进入与可能有偏信念下第一价格拍卖的识别AI 解读

This paper studies identification of first-price auctions where bidders enter auctions endogenously and may have biased beliefs about the valuation distribution. We analyze the symmetric independent private setup under three commonly used entry models. In the Levin-Smith entry model, the primitives are point-identified. In the Samuelson entry model, the primitives are identified only on a subset of the support of valuations. In the arbitrarily selective entry model, the…

面板回归模型的核范数正则化估计AI 解读

In this paper we investigate panel regression models with interactive fixed effects. We propose two new estimation methods that are based on minimizing convex objective functions. The first method minimizes the sum of squared residuals with a nuclear (trace) norm regularization. The second method minimizes the nuclear norm of the residuals. We establish the consistency of the two resulting estimators. Those estimators have a very important computational advantage compared to…

利用空间建模处理协变量测量误差AI 解读

We propose a new estimation methodology to address the presence of covariate measurement error by exploiting the availability of spatial data.The approach uses neighboring observations as repeated measurements, after suitably controlling for the random distance between the observations in a way that allows the use of operator diagonalization methods to establish identification.The method is applicable to general nonlinear models with potentially nonclassical errors and does…

集合识别 SVAR 预测误差方差分解的估计与推断AI 解读

We study estimation and inference of the Forecast Error Variance Decomposition (FEVD) in Structural Vector Autoregressions (SVARs) that are set-identified by internal and external (proxy) restrictions. The FEVD measures the importance of shocks for macroeconomic fluctuations and is thus key to business cycle analysis. We deliver three practitioner-oriented results. First, we characterize the endpoints of the FEVD as the extreme eigenvalues of a symmetric reduced-form matrix,…

工具变量外生性的一致有效检验AI 解读

This paper studies the limiting behavior of the test for instrument exogeneity in linear models when there is uncertainty about the strength of the identification signal. We consider the test for conditional moment restrictions with an expanding set of constructed instruments. We establish the uniform validity of the standard normal asymptotic approximation, under the null, of this specification test over all possible degrees of model identification. As a result, this allows…

动态随机系数模型的识别与估计AI 解读

Abstract I study linear panel data models with predetermined regressors (such as lagged dependent variables) where coefficients are individual-specific, allowing for heterogeneity in the effects of the regressors on the dependent variable. I show that the model is not point-identified in a short panel context but rather partially identified, and I characterize the identified sets for the mean, variance, and CDF of the coefficient distribution. This characterization is…

自适应划分因子分析AI 解读

Factor Analysis has traditionally been utilized across diverse disciplines to extrapolate latent traits that influence the behavior of multivariate observed variables. Historically, the focus has been on analyzing data from a single study, neglecting the potential study-specific variations present in data from multiple studies. Multi-study factor analysis has emerged as a recent methodological advancement that addresses this gap by distinguishing between latent traits shared…

对美联储的政治压力AI 解读

Abstract This paper combines new data and a narrative approach to identify variation in political pressure on the Federal Reserve. From archival records, I build a data set of personal interactions between U.S. Presidents and Fed officials between 1933 and 2016. Since personal interactions do not necessarily reflect political pressure, I develop a narrative identification strategy based on President Nixon’s pressure on Fed Chair Burns. I exploit this narrative through…

分簇网络连通性：测度框架及全球股票市场应用AI 解读

Network connections, both across and within markets, are central in countless economic contexts. In recent decades, a large literature has developed and applied flexible methods for measuring network connectedness and its evolution, based on variance decompositions from vector autoregressions (VARs), as in Diebold and Yilmaz (2014). Those VARs are, however, typically identified using full orthogonalization (Sims, 1980), or no orthogonalization (Koop et al., 1996; Pesaran and…

加性效应辅助学习AI 解读

Abstract It is quite popular nowadays for researchers and data analysts holding different datasets to seek assistance from each other to enhance their modelling performance. We consider a scenario where different learners hold datasets with potentially distinct variables, and their observations can be aligned by a nonprivate identifier. Their collaboration faces the following difficulties: first, learners may need to keep data values or even variable names undisclosed due…

析因双重差分AI 解读

We formulate factorial difference-in-differences (FDID), a research design that extends canonical difference-in-differences (DID) to settings in which an event affects all units. In many panel data applications, researchers exploit cross-sectional variation in a baseline factor alongside temporal variation in the event, but the corresponding estimand is often implicit and the justification for applying the DID estimator remains unclear. We frame FDID as a factorial design…

通过联合正则化深度神经网络估计单细胞异质基因网络AI 解读

Estimation of intracellular gene networks has been a critical component of single-cell transcriptomic data analysis, which can provide crucial insights into the complex interplay between genes, facilitating the discovery of the biological basis of human life at single-cell resolution. Despite notable achievements, existing methodologies often falter in their practicality, primarily due to their narrow focus on simplistic linear relationships and inadequate handling of…

SurvSTAAR：大规模全基因组测序中事件发生时间性状的罕见变异分析框架AI 解读

The increasing availability of large-scale, population-based whole-genome sequencing (WGS) data enables comprehensive analyses of rare genetic variants, which are crucial for unraveling the genetic mechanisms underlying complex traits and diseases. Time-to-event traits offer the advantage of capturing both diagnosis status and timing, facilitating the identification of genetic variants associated with age of onset, disease progression, and lifespan. However, existing methods…

人机交互的收益：混杂环境中的超级策略学习AI 解读

As AI becomes more prevalent throughout society, effective methods of integrating humans and AI systems that leverage their respective strengths and mitigate risk have become an important priority. In this article, we introduce the paradigm of super policy learning that takes advantage of Human-AI interaction for data driven sequential decision making. This approach uses the observed action, either from AI or humans, as input for achieving a stronger oracle in policy…

基于汇总数据、消除赢家诅咒的稳健孟德尔随机化AI 解读

In the past decade, the increased availability of genome-wide association studies summary data has popularized Mendelian Randomization (MR) for conducting causal inference. MR analyses, incorporating genetic variants as instrumental variables, are known for their robustness against reverse causation bias and unmeasured confounders. Nevertheless, classical MR analyses using summary data may still produce biased causal effect estimates due to the winner's curse and pleiotropy…

多重矩等式检验的功效增强：超越二范数与无穷范数AI 解读

Tests based on the 2- and ∞-norm have received considerable attention in high-dimensional testing problems, as they are powerful against dense and sparse alternatives, respectively. The power enhancement principle of Fan et al. (2015) combines these two norms to construct improved tests that are powerful against both types of alternatives. In the context of testing whether a candidate parameter satisfies a large number of moment equalities, we construct tests that harness…

借助辅助总体工具变量识别与多重稳健估计因果效应AI 解读

Estimating causal effects in a target population with unmeasured confounders is challenging, especially when instrumental variables (IVs) are unavailable. However, IVs from auxiliary populations with similar problems can help infer causal effects in the target population. While the homogeneous conditional average treatment effect assumption has been widely used for effect transportability, it has not been explored in IV-based data fusion. We include it as a basic approach,…

面向分布福利的政策学习AI 解读

In this paper, we explore optimal treatment allocation policies that target distributional welfare. Most literature on treatment choice has considered utilitarian welfare based on the conditional average treatment effect (ATE). While average welfare is intuitive, it may yield undesirable allocations especially when individuals are heterogeneous (e.g., with outliers)—the very reason individualized treatments were introduced in the first place. This observation motivates us to…

双重稳健的交错双重差分组别—时期条件平均处理效应一致置信带AI 解读

We consider a panel data analysis to examine the heterogeneity in treatment effects with respect to groups, periods, and a pre-treatment covariate of interest in the staggered difference-in-differences setting of Callaway and Sant’Anna. Under standard identification conditions, a doubly robust estimand conditional on the covariate identifies the group-time conditional average treatment effect given the covariate. Focusing on the case of a continuous covariate, we propose a…

内生性下的极端分位数处理效应AI 解读

We introduce a novel method for estimating and conducting inference about extreme quantile treatment effects (QTEs) in the presence of endogeneity. Our approach is applicable to a broad range of empirical research designs, including instrumental variables design and regression discontinuity design, among others. By leveraging regular variation and subsampling, the method ensures good performance even in extreme tails, where data may be sparse. Simulation studies confirm the…