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以共同协变量平滑的函数系数面板模型AI 解读

Behavior at the individual level in panels is often influenced by aspects of the system in aggregate. In particular, the interaction between individual-specific explanatory variables and an individual dependent variable may be affected by ‘global’ variables that are relevant in decision making and shared communally by all individuals in the sample. To capture such behavioral features, we employ a functional coefficient panel model in which certain communal covariates may…

企业财务政策中的网络效应AI 解读

We present a spatial econometrics framework for estimating peer effects in capital structure. This approach exploits the heterogeneous and intransitive nature of peer networks to identify economically informative structural coefficients. In models of leverage levels, we detect significant peer-effect leverage coefficients that are on the order of 0.20, indicating a moderate but substantive level of strategic complementarity in capital structure decisions. We argue that prior…

首次公开募股的同伴效应AI 解读

This study investigates whether a private firm’s decision to go public affects the IPO decisions of its competitors. Using detailed data from the drug development industry, we identify a private firm’s direct competitors at a precise level through a novel approach using similarity in drug development projects based on disease targets. The analysis shows that a private firm is significantly more likely to go public after observing the recent IPO of a direct competitor, and…

嵌套 Logit 模型的随机效用表示AI 解读

In this paper, we give a two-line proof of a long-standing conjecture of Ben-Akiva in his 1973 PhD thesis regarding the random utility representation of the nested logit model, thus providing a renewed and straightforward textbook treatment of that model. As an application, we provide a closed-form formula for the correlation between two Fréchet random variables coupled by a Gumbel copula.

初级医疗与急诊二级医疗需求的动态相互依赖：隐马尔可夫方法AI 解读

Summary This paper develops an extension of the class of finite mixture models for longitudinal count data to the bivariate case by using a hidden Markov chain approach. The model allows for disentangling unobservable time‐varying heterogeneity from the dynamic effect of utilisation of primary and secondary care and measuring their potential substitution effect. Three points of supports adequately describe the distribution of the latent states suggesting the existence of…

利用含截面依赖的函数系数面板模型检验资本资产定价模型AI 解读

This paper proposes a functional-coefficient panel data model with cross-sectional dependence motivated by re-examining the empirical performance of conditional capital asset pricing model. In order to characterize the time-varying property of assets’ betas and alpha, our proposed model allows the betas to be unknown functions of some macroeconomic and financial instruments. Moreover, a common factor structure is introduced to characterize cross-sectional dependence which is…

职业培训的动态处理效应AI 解读

Summary This paper estimates the dynamic returns to job training. We posit a model of sequential training participation, where decisions and outcomes depend on observed and unobserved characteristics. We analyze different treatment effects, including policy relevant parameters, and link them to continuation values and latent skills. The empirical analysis exploits administrative data combining job training records, matched employee‐employer information, and pre‐labor market…

求职援助项目的动态评估AI 解读

Summary This paper evaluates a job search assistance program for unemployed teachers where the assignment to the program is dynamic. We discuss the methodology of estimating dynamic treatment effects and identification conditions. In the empirical analysis, we use administrative data from a unique institutional environment in which we observe all variables determining assignment to the job search assistance program. This allows us to compare results from a dynamic…

基于重抽样统计量的依赖稳健推断AI 解读

Summary We develop inference procedures robust to general forms of weak dependence. The procedures utilize test statistics constructed by resampling in a manner that does not depend on the unknown correlation structure of the data. We prove that the statistics are asymptotically normal under the weak requirement that the target parameter can be consistently estimated at the parametric rate. This holds for regular estimators under many well‐known forms of weak dependence and…

含隐变量的因果图模型中的高效调整集AI 解读

Summary We study the selection of adjustment sets for estimating the interventional mean under a point exposure dynamic treatment regime, that is, a treatment rule that depends on the subject’s covariates. We assume a nonparametric causal graphical model with, possibly, hidden variables and at least one adjustment set comprised of observable variables. We provide the definition of a valid adjustment set for a point exposure dynamic treatment regime, which generalizes the…

多重处理与二元结果下因果效应的可识别性AI 解读

Summary Unobserved confounding presents a major threat to causal inference in observational studies. Recently, several authors have suggested that this problem could be overcome in a shared confounding setting where multiple treatments are independent given a common latent confounder. It has been shown that under a linear Gaussian model for the treatments, the causal effect is not identifiable without parametric assumptions on the outcome model. In this note, we show that…

最小化及其他协变量自适应随机化下的平均处理效应推断AI 解读

Summary Covariate-adaptive randomization schemes such as minimization and stratified permuted blocks are often applied in clinical trials to balance treatment assignments across prognostic factors. The existing theory for inference after covariate-adaptive randomization is mostly limited to situations where a correct model between the response and covariates can be specified or the randomization method has well-understood properties. Based on stratification with covariate…

网络数据中协变量的降维AI 解读

Summary A problem of major interest in network data analysis is to explain the strength of connections using context information. To achieve this, we introduce a novel approach, called network-supervised dimension reduction, in which covariates are projected onto low-dimensional spaces to reveal the linkage pattern without assuming a model. We propose a new loss function for estimating the parameters in the resulting linear projection, based on the notion that closer…

高维分位数回归：卷积平滑与凹正则化AI 解读

Abstract ℓ 1 -penalized quantile regression (QR) is widely used for analysing high-dimensional data with heterogeneity. It is now recognized that the ℓ1-penalty introduces non-negligible estimation bias, while a proper use of concave regularization may lead to estimators with refined convergence rates and oracle properties as the signal strengthens. Although folded concave penalized M-estimation with strongly convex loss functions have been well studied, the extant…

一般干扰下因果效应随机化检验的图论方法AI 解读

Abstract Interference exists when a unit's outcome depends on another unit's treatment assignment. For example, intensive policing on one street could have a spillover effect on neighbouring streets. Classical randomization tests typically break down in this setting because many null hypotheses of interest are no longer sharp under interference. A promising alternative is to instead construct a conditional randomization test on a subset of units and assignments for which a…

多变量纵向与生存数据的半参数潜在类别模型AI 解读

In long-term follow-up studies, data are often collected on repeated measures of multivariate response variables as well as on time to the occurrence of a certain event. To jointly analyze such longitudinal data and survival time, we propose a general class of semiparametric latent-class models that accommodates a heterogeneous study population with flexible dependence structures between the longitudinal and survival outcomes. We combine nonparametric maximum likelihood…

超图社群结构的检验AI 解读

Many complex networks in the real world can be formulated as hypergraphs where community detection has been widely used. However, the fundamental question of whether communities exist or not in an observed hypergraph remains unclear. This work aims to tackle this important problem. Specifically, we systematically study when a hypergraph with community structure can be successfully distinguished from its Erdős–Rényi counterpart, and propose concrete test statistics when the…

利用分布不连续协变量检验基于可观测变量的选择假设AI 解读

We present a test of the selection on observables assumption that neither requires instruments nor excluded covariates from the structural function. Instead, we rely on the presence of a discontinuously distributed variable among the set of controls. We develop formal testing procedures for a non-parametric additively separable model for binary and finite treatment variables. We also outline a nonparametric nonseparable extension. Our test is easy to implement and should be…

完全信息有序反应博弈中的推断AI 解读

We study inference in complete information games with discrete strategy spaces. Unlike binary games, we allow for rich strategy spaces and we only assume that they are ordinal in nature. We derive observable implications of equilibrium play under mild shape restrictions on payoff functions, and we characterize sharp identified sets for model parameters. We propose a novel inference method based on a test statistic that embeds conditional moment inequalities implied by…

参数存在跳跃不连续的面板模型：小波方法AI 解读

While a substantial literature on structural break change point analysis exists for univariate time series, research on large panel data models has not been as extensive. In this paper, a novel method for estimating panel models with multiple structural changes is proposed. The breaks are allowed to occur at unknown points in time and may affect the multivariate slope parameters individually. Our method adapts Haar wavelets to the structure of the observed variables in order…

半参数模型系数的识别及集体家庭模型应用AI 解读

We prove identification of coefficients for a set of semiparametric specifications that are related to multiple index models. Potential applications of these results include models of observed heterogeneity in production functions and in consumer demand systems. We then generalize these results to identify a class of collective household consumption models. We extend the existing literature by proving point identification, rather than the weaker generic identification, of…

不可观测选择集下的多项选择模型估计AI 解读

This paper proposes a new approach to estimating multinomial choice models when each consumer’s actual choice set is unobservable but could be bounded by two known sets, i.e., the largest and smallest possible choice sets. The bounds on choice set, combined with a monotonicity property derived from utility maximization, imply a system of inequality restrictions on observed choice probabilities that could be used to identify and estimate the model. A key insight is that the…

含不可观测异质性与多重均衡的动态博弈识别AI 解读

This paper provides sufficient conditions for nonparametrically identifying dynamic games with incomplete information, allowing for multiple equilibria and payoff-relevant unobservables. Our identification involves two steps. We first identify the equilibrium conditional choice probabilities and state transitions using the Markov property and four-period data. The first step of our identification relies on eigenvalue-eigenvector decomposition, and thus incurs the same issue…

第一价格密封拍卖中的风险厌恶检验AI 解读

We consider testing for risk aversion in first-price sealed-bid auctions with symmetric bidders and independent private values. We impose several restrictions on the parameter space, which are all implied by Guerre et al. (2009)’s exclusion restriction, and we articulate what restrictions are needed for our test to control the limiting size and to be pointwise consistent. Critical values can be obtained from the standard normal distribution. We also analyze local-power…