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反倾销关税的就业后果：来自巴西的经验AI 解读

Abstract How do import tariffs affect employment? We develop an empirical strategy to identify the effects of tariffs using a difference-in-differences strategy, comparing antidumping (AD) investigations resulting in AD tariffs to those not resulting in AD tariffs. We find that an AD tariff decreases imports and increases employment in the protected sector. Moreover, employment in downstream firms decreases, while upstream firms are unaffected because the protected sector…

干扰与复杂实验设计下的优化方差估计AI 解读

Unbiased and consistent variance estimators generally do not exist for design-based treatment effect estimators because experimenters never observe more than one potential outcome for any unit. The problem is exacerbated by interference and complex experimental designs. Experimenters must accept conservative variance estimators in these settings, but they can strive to minimize the conservativeness. In this paper, we show that the task of constructing a minimally…

全球化与发展阶梯：推向顶端还是困于底部？AI 解读

Abstract We study the relationship between international trade and development in a model where countries differ in their capability, goods differ in their complexity, and capability growth is a function of a country’s pattern of specialization. Theoretically, we show that it is possible for international trade to increase capability growth in all countries and, in turn, to push all countries up the development ladder. This occurs if (i) shifting employment towards more…

失业保险潜在领取期限变化的微观与宏观效应AI 解读

Abstract We quantify micro and macro effects of changes in the potential benefit duration (PBD) in unemployment insurance. In Poland, the PBD is 12 months for the newly unemployed if the previous year’s county unemployment rate is more than 150% of the national average, and 6 months otherwise. We exploit this cut-off using regression discontinuity estimates on registry data containing the universe of unemployed from 2005 to 2019. For those whose PBD is directly affected by…

结构动态离散选择模型的反事实分析AI 解读

Abstract Discrete choice data allow researchers to recover differences in utilities, but these differences may not suffice to identify policy-relevant counterfactuals of interest. In fact, in the case of dynamic discrete choice models, only a narrow set of counterfactuals are point-identified. In this paper, we explore how much one can learn about counterfactual outcomes of interest within this framework. We focus on the partial identification of counterfactuals, while…

高维混杂下条件平均处理效应的非参数估计AI 解读

SUMMARY This study considers the practically important case of nonparametrically estimating heterogeneous average treatment effects that vary with a limited number of discrete and continuous covariates in a selection-on-observables framework where the number of possible confounders is very large. We propose a two-step estimator for which the first step is estimated by machine learning. We show that this estimator has desirable statistical properties such as consistency,…

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…

二元结果高维广义线性模型中的因果推断AI 解读

Summary This paper proposes a debiased estimator for causal effects in high-dimensional generalized linear models with binary outcomes and general link functions. The estimator augments a regularized regression plug-in with weights computed from a single convex optimization that approximately balances link-derivative-weighted covariates and controls variance; it does not rely on estimated propensity scores. Under standard conditions, the estimator is $\sqrt{n}$-consistent…

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…

跨异质地点的因果效应迁移估计AI 解读

We consider the problem of extrapolating treatment effects across heterogeneous populations (“sites”/“contexts”). We consider an idealized scenario in which the researcher observes cross-sectional data for a large number of units across several “experimental” sites in which an intervention has already been implemented to a new “target” site for which a baseline survey of unit-specific, pre-treatment outcomes and relevant attributes is available. Our approach treats the…

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…

短面板中的潜在因子分析AI 解读

We develop a pseudo maximum likelihood method for latent factor analysis in short panels without imposing sphericity nor Gaussianity. We derive an asymptotically uniformly most powerful invariant test for the number of factors. On a large panel of monthly U.S. stock returns, we separate month after month systematic and idiosyncratic risks in short subperiods of bear vs. bull market. We observe an uptrend in the paths of total and idiosyncratic volatilities. The systematic…

大规模网络的降秩多元空间自回归模型AI 解读

In spatial and social network analysis, multivariate spatial autoregressive (MSAR) models are effective tools for analyzing network data with multivariate responses. When the dimension of the response is divergent, however, the number of unknown parameters in an MSAR increases at a rate that is proportional to the square of the dimensionality, which poses significant challenges to the model estimation process. To address this issue, we propose a novel reduced-rank MSAR model…

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…

是否应利用辅助网络信息改进大型协方差矩阵估计？AI 解读

This paper uses the auxiliary network information, observed in addition to the original sample, to infer latent network structures in the population correlation matrix and thus improve high-dimensional covariance matrix estimation. Building on estimated Location Indicator and Relative Importance matrices, we propose two Network-Guided estimators. Network-Guided Thresholding uses auxiliary network data to regularize the large and small elements in the sample covariance…

含交互固定效应的非平衡面板模型估计与推断AI 解读

This paper establishes the inferential theory for unbalanced panel data models with interactive fixed effects. We propose a two-step estimation algorithm with the first step obtaining an initial consistent estimator followed by an alternating maximization procedure. We prove that the alternating maximization procedure is a contractionary mapping and the final estimator is asymptotically normal, as long as the initial estimator is consistent. We also develop analytical bias…

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…

结合目标学习推断的在线元层级自适应设计框架AI 解读

Adaptive designs are increasingly used in clinical trials and online experiments to improve participant outcomes by dynamically updating treatment allocation as data accumulate. In practice, experimenters often consider multiple candidate designs, each with distinct trade-offs. However, typically only one design is implemented at a time, leaving benefits and costs of alternative designs unobserved and unquantified. To address this, we propose a novel meta-level adaptive…

少即是多：二元反馈可能更有利于排序恢复AI 解读

Paired comparison data, where users evaluate items in pairs, play a central role in ranking and preference learning tasks. While ordinal comparison data intuitively offer richer information than binary comparisons, this paper challenges that conventional wisdom. We propose a general parametric framework for modeling ordinal paired comparisons without ties. The model adopts a generalized additive structure, featuring a link function that quantifies the preference difference…

因果 K 均值聚类AI 解读

Abstract Causal effects are often characterized at the population level, which can mask important heterogeneity across latent subgroups. Since the subgroup structure is unknown, identifying and evaluating subgroup specific effects is substantially more challenging than standard population level analysis. We address this problem by proposing Causal k-Means Clustering, a framework that uses k-means clustering ideas to recover unknown subgroup structure from individual level…

随机实验中协变量平衡的高斯化设计优化AI 解读

Abstract Achieving covariate balance in randomized experiments enhances the precision of treatment effect estimation. However, existing methods often require heuristic adjustments based on domain knowledge and are primarily developed for binary treatments. This paper presents Gaussianized Design Optimization, a novel framework for optimally balancing covariates in experimental design. The core idea is to Gaussianize the treatment assignments: we model treatments as…

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…

具有依赖边的自回归网络AI 解读

Abstract We propose an autoregressive framework for modelling dynamic networks with dependent edges. It encompasses models that accommodate, for example, transitivity, degree heterogenenity, and other stylized features often observed in real network data. By assuming the edges of networks at each time are independent conditionally on their lagged values, the models, which exhibit a close connection with temporal exponential random graph models, facilitate both simulation and…