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因子网络自回归AI 解读

We propose a factor network autoregressive (FNAR) model for time series with complex network structures. The coefficients of the model reflect many different types of connections between economic agents (“multilayer network”), which are summarized into a smaller number of network matrices (“network factors”) through a novel tensor-based principal component approach. We provide consistency and asymptotic normality results for the estimation of the factors, their loadings, and…

半参数时变面板数据模型的估计与推断AI 解读

This article introduces a new semiparametric panel data model that accounts for time-varying coefficients and aligns with recent advancements in factor models featuring nonparametric loading functions. We propose a profile marginal integration (PMI) method to jointly estimate the unknown quantities in a series of easily implementable steps. The asymptotic properties of these estimators are established. Additionally, we provide a hypothesis test to assess the validity of…

观察性研究中多值处理政策学习的模型选择AI 解读

This study investigates the policy learning problem in observational studies, where the treatment variable can be multivalued and the propensity scores are unknown. We approximate the optimal policy in a global policy class with infinite complexity (VC/Natarajan) dimension, using a sequence of sieve policy classes with finite complexity dimension. The optimal policy within each sieve class is estimated by maximizing the empirical welfare, constructed through the doubly…

生产者—消费者模型识别与推断的显示偏好方法AI 解读

This paper provides a new identification result for a large class of models in which consumers participate in production. I show that consumer preferences are necessary and sufficient to identify production functions through cross-equation restrictions implied by first-order conditions. In addition, I derive a nonparametric revealed preference characterization of the class of models that exhausts its empirical implications. Finally, I use a novel and easy-to-apply inference…

极端连续处理效应的估计与推断AI 解读

This article studies estimation and inference for the treatment effect in deep tails of the potential outcome distributions corresponding to a continuously valued treatment, namely the extreme continuous treatment effect. We consider two measures for the tail characteristics: the quantile function and the tail mean function defined as the conditional mean beyond a quantile level. Then, for a quantile level close to 1, we define the extreme quantile treatment effect (EQTE)…

裂区设计中的再随机化与协变量调整AI 解读

Split-plot designs are widely used in agricultural, industrial, social, and biomedical experiments to accommodate hard-to-change factors. Given multiple factors of interest and experimental units that are nested within groups, the design assigns a subset of the factors by a cluster randomization at the group level and assigns the remaining factors by a stratified randomization at the unit level. The randomization mechanism ensures covariate balance on average at both the…

受配给约束的生育：子女数量—质量权衡中的处理效应异质性AI 解读

We develop a generalized theory of rationed fertility to analyze treatment effect heterogeneity in the child quantity-quality trade-off. An exogenous increase in fertility can be either desired or undesired. We derive a positive rationing income effect on child quality for desired fertility increases but a negative rationing income effect for undesired fertility increases. We propose an econometric framework to identify treatment effects of desired and undesired fertility…

通过神经对抗不变性学习从异质环境中追寻因果关系AI 解读

Pursuing causality from data is a fundamental problem in scientific discovery, treatment intervention, and transfer learning. This paper introduces a novel algorithmic method for addressing nonparametric invariance and causality learning in regression models across multiple environments, where the joint distribution of response variables and covariates varies, but the conditional expectations of outcome given an unknown set of quasi-causal variables are invariant. The…

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Plackett–Luce 模型中似然估计量的统一分析AI 解读

The Plackett–Luce model has been extensively used for rank aggregation in social choice theory. A central statistical question in this model concerns estimating the utility vector that governs the model’s likelihood. In this paper, we investigate the asymptotic theory of utility vector estimation by maximizing different types of likelihood, such as full, marginal, and quasi-likelihood. Starting from interpreting the estimating equations of these estimators to gain some…

函数依赖在空间计量经济学中的应用AI 解读

In this paper, we generalize the concept of functional dependence (FD) from time series (see Wu [2005, Proceedings of the National Academy of Sciences 102, 14150–14154]) and stationary random fields (see El Machkouri, Volný, and Wu [2013, Stochastic Processes and Their Applications 123, 1–14]) to nonstationary spatial processes. Within conventional settings in spatial econometrics, we define the concept of spatial FD measure and establish a moment inequality, an exponential…

空间自回归模型中的异方差稳健设定检验AI 解读

Spatial autoregressive (SAR) and related models offer flexible yet parsimonious ways to model spatial and network interactions. SAR specifications typically rely on a particular parametric functional form and an exogenous choice of the so-called spatial weight matrix with only limited guidance from theory in making these specifications. Also, the choice of a SAR model over other alternatives, such as spatial Durbin (SD) or spatial lagged X (SLX) models, is often arbitrary,…

需求的实解析离散选择模型：理论与含义AI 解读

We demonstrate that a large class of discrete choice models of demand can be approximated by real analytic demand models. We obtain this result by combining (i) a novel real analytic property of the mixed logit and the mixed probit models with any distribution of random coefficients and (ii) an approximation property of finite mixtures of Gumbel and Gaussian distributions. To illustrate some of the implications of this result, we discuss how real analyticity facilitates…

有限重叠的检验AI 解读

Extreme propensity scores arise in observational studies when treated and control units have very different characteristics. This is commonly referred to as limited overlap . In this paper, we propose a formal statistical test that helps assess the degree of limited overlap. Rejecting the null hypothesis in our test indicates either no or very mild degree of limited overlap and hence reassures that standard treatment effect estimators will be well behaved. One distinguishing…

对序列依赖稳健的横截面相关自助法检验AI 解读

Summary We propose a novel bootstrap-based test for cross-sectional dependence (CD) in panel models, maintaining robustness against serial dependence. While serial dependence is common in panel data, existing tests often assume serial independence. Our cluster wild bootstrap CD test procedure mirrors Pesaran’s original CD test and is very simple to implement. This procedure preserves serial dependence while testing for cross-sectional independence. Theoretical validity is…

半参数模型中的交叉拟合经验似然AI 解读

Summary We propose a new cross-fitted empirical likelihood (CFEL) inference procedure for low-dimensional parameters in the presence of complex, infinite dimensional nuisance parameters that may be estimated by modern machine-learning methods. Relying on the Neyman orthogonal moment condition and cross-fitting as a sample-splitting procedure, we show that our CFEL statistic is asymptotically pivotal under weak conditions often invoked in Wald-type approaches. Three examples…

说服研究中的二元工具变量模型：刻画依从者的说服类型AI 解读

Summary In an empirical study of persuasion, researchers often use a binary instrument to encourage individuals to consume information and take some action. We show that, with a binary Imbens–Angrist instrumental variable model and the monotone treatment response assumption, it is possible to identify the joint distribution of potential outcomes among compliers. This is necessary to identify the percentage of mobilized voters and their statistical characteristic defined by…

面向大型数据集的双样本量估计量AI 解读

Summary In generalized method of moments (GMM) estimators, moment conditions with additive error terms involve an observed component and a predicted component. If the predicted component is computationally costly to evaluate, it may not be feasible to estimate the model with all the available data. We propose a simple two-sample ‘large–small’ size estimator that uses the full dataset for the computationally cheap observed component, but a reduced sample size for the…

使Lee界更稳健且更紧：随机单调性与对称性下的样本选择模型AI 解读

Summary In the presence of sample selection, Lee’s (2009, Review of Economic Studies 76, 1071–102) non-parametric bounds are a popular tool for estimating a treatment effect. However, the Lee bounds rely on the monotonicity assumption, the empirical validity of which is sometimes unclear. Furthermore, the bounds are often regarded to be wide and less informative even under monotonicity. To address these issues, this study introduces a stochastic version of the monotonicity…

提高观察性区组设计敏感度的条件化策略AI 解读

Abstract In an observational block design, there are I blocks of J individuals, typically with one treated individual and J−1 controls; however, unlike a randomized block design, individuals were not randomly assigned to treatment or control. To be convincing, an observational block design must demonstrate that an ostensible treatment effect is not actually a consequence of small or moderate unmeasured biases of treatment assignment in the absence of a treatment effect. It…

面向处理效应异质性学习的自适应实验AI 解读

Abstract Understanding treatment effect heterogeneity has become an increasingly popular task in various fields, as it helps design personalized advertisements in e-commerce or targeted treatment in biomedical studies. However, most of the existing work in this research area focused on either analysing observational data based on strong causal assumptions or conducting post hoc analyses of randomized controlled trial data, and there has been limited effort dedicated to the…

半参数后验修正AI 解读

Abstract We present a new approach to semiparametric inference using corrected posterior distributions. The method allows us to leverage the adaptivity, regularization, and predictive power of nonparametric Bayesian procedures to estimate low-dimensional functionals of interest without being restricted by the holistic Bayesian formalism. Starting from a conventional posterior on the whole data-generating distribution, we correct the marginal posterior for each functional of…

交互项模型中的遗漏变量偏误：一则警示AI 解读

Abstract We highlight that analyses using interaction terms to study treatment effect heterogeneity are susceptible to a form of omitted variable bias that is often overlooked in economics. Unlike most instances of omitted variable bias, the omitted variables in this case are available to the researcher but were not included in the model. We demonstrate that this exclusion matters based on a replication of 205 estimates across seventeen papers published in the American…

选举中的执政更替AI 解读

Abstract In most national elections, voters face a key choice between continuity and change. Electoral turnovers occur when the incumbent candidate or party fails to win reelection. To understand how turnovers affect national outcomes, we study all presidential and parliamentary elections held globally between 1946 and 2018. We document the prevalence of turnovers over time and estimate their effects on economic performance, human development, and the quality of democracy.…

校准参数的标准误AI 解读

Abstract Calibration, the practice of choosing the parameters of a structural model to match certain empirical moments, can be viewed as minimum distance estimation. Existing standard error formulas for such estimators require a consistent estimate of the correlation structure of the empirical moments, which is often unavailable in practice. Instead, the variances of the individual empirical moments are usually readily estimable. Using only these variances, we derive…