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社会互动下的处理响应：通过单调比较静态实现部分识别AI 解读

This paper studies (nonparametric) partial identification of treatment response with social interactions. It imposes conditions motivated by economic theory on the primitives of the model, that is, the structural equations, and shows that they imply shape restrictions on the distribution of potential outcomes via monotone comparative statics. The econometric framework is tractable and allows for counterfactual predictions in models with multiple equilibria. Under three sets…

通用奇异值阈值法的矩阵估计AI 解读

Consider the problem of estimating the entries of a large matrix, when the observed entries are noisy versions of a small random fraction of the original entries. This problem has received widespread attention in recent times, especially after the pioneering works of Emmanuel Candès and collaborators. This paper introduces a simple estimation procedure, called Universal Singular Value Thresholding (USVT), that works for any matrix that has “a little bit of structure.”…

区间数据下半参数二元响应模型的推断AI 解读

This paper studies the semiparametric binary response model with interval data investigated by Manski and Tamer (2002). In this partially identified model, we propose a new estimator based on MT’s modified maximum score (MMS) method by introducing density weights to the objective function, which allows us to develop asymptotic properties of the proposed set estimator for inference. We show that the density-weighted MMS estimator converges at a nearly cube-root-n rate. We…

含样本选择的乘法误差模型AI 解读

This paper presents a simple approach to deal with sample selection in models with multiplicative errors. Models for non-negative limited dependent variables such as counts fit this framework. The approach builds on a specification of the conditional mean of the outcome only and is, therefore, semiparametric in nature. GMM estimators are constructed for both cross-section data and for panel data. We derive distribution theory and present Monte Carlo evidence on the…

空间权重矩阵内生时的空间自回归模型估计AI 解读

The spatial autoregressive (SAR) model is a standard tool for analyzing data with spatial correlation. Conventional estimation methods rely on the key assumption that the spatial weight matrix is strictly exogenous, which would likely be violated in some empirical applications where spatial weights are determined by economic factors. This paper presents model specification and estimation of the SAR model with an endogenous spatial weight matrix. We provide three estimation…

模糊性重要吗？多重先验递归效用资产定价模型的估计AI 解读

This paper considers asset pricing models with stochastic differential utility incorporating decision makers׳ concern with ambiguity on true probability measure. Under a representative agent setting, we empirically evaluate alternative preference specifications including a multiple-priors recursive utility. We find that relative risk aversion is estimated around 1–8 with ambiguity aversion and 7.4–15 without ambiguity aversion. Estimated ambiguity aversion is both…

税收与公司政策：准自然实验的证据AI 解读

ABSTRACT We document important interactions between tax incentives and corporate policies using a “quasi natural experiment” provided by a surprise announcement that imposed corporate taxes on a group of Canadian publicly traded firms. The announcement caused a dramatic decrease in value. Prospective tax shields partially offset the losses, adding 4.6% to firm value on average, and vary with the tax status of the marginal investor. Further, firms adjust leverage, payout,…

Haavelmo 与因果演算的形成AI 解读

Haavelmo was the first to recognize the capacity of economic models to guide policies. This paper describes some of the barriers that Haavelmo’s ideas have had (and still have) to overcome and lays out a logical framework that has evolved from Haavelmo’s insight and matured into a coherent and comprehensive account of the relationships between theory, data, and policy questions. The mathematical tools that emerge from this framework now enable investigators to answer complex…

Haavelmo 之后的因果分析AI 解读

Haavelmo’s seminal 1943 and 1944 papers are the first rigorous treatment of causality. In them, he distinguished the definition of causal parameters from their identification. He showed that causal parameters are defined using hypothetical models that assign variation to some of the inputs determining outcomes while holding all other inputs fixed. He thus formalized and made operational Marshall’s (1890) ceteris paribus analysis. We embed Haavelmo’s framework into the…

均衡数量的非参数推断AI 解读

This paper proposes an estimator and develops an inference procedure for the number of roots of functions that are non‐parametrically identified by conditional moment restrictions. It is shown that a smoothed plug‐in estimator of the number of roots is superconsistent under i.i.d. asymptotics, but asymptotically normal under non‐standard asymptotics. The smoothed estimator is furthermore asymptotically efficient relative to a simple plug‐in estimator. The procedure proposed…

隔离相异指数的更可靠统计推断AI 解读

The most widely used measure of segregation is the so‐called dissimilarity index. It is now well understood that this measure also reflects randomness in the allocation of individuals to units (i.e. it measures deviations from evenness, not deviations from randomness). This leads to potentially large values of the segregation index when unit sizes and/or minority proportions are small, even if there is no underlying systematic segregation. Our response to this is to produce…

估计函数可能不可微的M估计量稳健假设检验AI 解读

We propose a new robust hypothesis test for (possibly non‐linear) constraints on M‐estimators with possibly non‐differentiable estimating functions. The proposed test employs a random normalizing matrix computed from recursive M‐estimators to eliminate the nuisance parameters arising from the asymptotic covariance matrix. It does not require consistent estimation of any nuisance parameters, in contrast with the conventional heteroscedasticity‐autocorrelation consistent…

社会网络中的多主体推断：有限总体学习方法AI 解读

When people in a society want to make inference about some parameter, each person may want to use data collected by other people. Information (data) exchange in social networks is usually costly, so to make reliable statistical decisions, people need to trade off the benefits and costs of information acquisition. Conflicts of interests and coordination problems will arise in the process. Classical statistics does not consider people's incentives and interactions in the data…

非可忽略单调缺失纵向数据的灵活贝叶斯方法及精神分裂症试验应用AI 解读

We develop a Bayesian nonparametric model for a longitudinal response in the presence of nonignorable missing data. Our general approach is to first specify a working model that flexibly models the missingness and full outcome processes jointly. We specify a Dirichlet process mixture of missing at random (MAR) models as a prior on the joint distribution of the working model. This aspect of the model governs the fit of the observed data by modeling the observed data…

分位数回归的模型稳健设计AI 解读

We give methods for the construction of designs for regression models, when the purpose of the investigation is the estimation of the conditional quantile function, and the estimation method is quantile regression. The designs are robust against misspecified response functions, and against unanticipated heteroscedasticity. The methods are illustrated by example, and in a case study in which they are applied to growth charts.

高维工具变量回归的正则化方法及遗传基因组学应用AI 解读

In genetical genomics studies, it is important to jointly analyze gene expression data and genetic variants in exploring their associations with complex traits, where the dimensionality of gene expressions and genetic variants can both be much larger than the sample size. Motivated by such modern applications, we consider the problem of variable selection and estimation in high-dimensional sparse instrumental variables models. To overcome the difficulty of high…

兼顾效率与伦理的协变量调整响应自适应设计统一族AI 解读

Response-adaptive designs have recently attracted more and more attention in the literature because of its advantages in efficiency and medical ethics. To develop personalized medicine, covariate information plays an important role in both design and analysis of clinical trials. A challenge is how to incorporate covariate information in response-adaptive designs while considering issues of both efficiency and medical ethics. To address this problem, we propose a new and…

结构化 logistic–normal 混合模型的亚组分析推断AI 解读

In this article, we propose a statistical model for the purpose of identifying a subgroup that has an enhanced treatment effect as well as the variables that are predictive of the subgroup membership. The need for such subgroup identification arises in clinical trials and in market segmentation analysis. By using a structured logistic-normal mixture model, our proposed framework enables us to perform a confirmatory statistical test for the existence of subgroups, and at the…

分位数相关与分位数自回归建模AI 解读

In this article, we propose two important measures, quantile correlation (QCOR) and quantile partial correlation (QPCOR). We then apply them to quantile autoregressive (QAR) models, and introduce two valuable quantities, the quantile autocorrelation function (QACF) and the quantile partial autocorrelation function (QPACF). This allows us to extend the Box–Jenkins three-stage procedure (model identification, model parameter estimation, and model diagnostic checking) from…

联合观测与干预数据估计有向无环图的干预马尔可夫等价类AI 解读

Summary In many applications we have both observational and (randomized) interventional data. We propose a Gaussian likelihood framework for joint modelling of such different data types, based on global parameters consisting of a directed acyclic graph and corresponding edge weights and error variances. Thanks to the global nature of the parameters, maximum likelihood estimation is reasonable with only one or few data points per intervention. We prove consistency of the…

纵向研究的均值、方差与相关联合建模AI 解读

Summary In longitudinal studies, it is of fundamental importance to understand the dynamics in the mean function, variance function and correlations of the repeated or clustered measurements. For modelling the covariance structure, Cholesky-type decomposition-based approaches have been demonstrated to be effective. However, parsimonious approaches for directly revealing the correlation structure between longitudinal measurements remain less well explored, and existing joint…

重复测量与事件时间结局的联合建模：灵活设定与精确似然推断AI 解读

Summary Random effects or shared parameter models are commonly advocated for the analysis of combined repeated measurement and event history data, including dropout from longitudinal trials. Their use in practical applications has generally been limited by computational cost and complexity, meaning that only simple special cases can be fitted by using readily available software. We propose a new approach that exploits recent distributional results for the extended skew…

调整半竞争风险所致依赖删失的分位数回归AI 解读

Summary We study quantile regression when the response is an event time subject to potentially dependent censoring. We consider the semicompeting risks setting, where the time to censoring remains observable after the occurrence of the event of interest. Although such a scenario frequently arises in biomedical studies, most of current quantile regression methods for censored data are not applicable because they generally require the censoring time and the event time to be…

含可分离与不可分离时空滤波的固定效应面板模型估计AI 解读

This paper considers a quasi-maximum likelihood estimation for a linear panel data model with time and individual fixed effects, where the disturbances have dynamic and spatial correlations which might be spatially stable or unstable. We first consider both separable and nonseparable space–time filters for the stable model. The separable space–time filter is subject to a parametric restriction which results in relative computational simplicity. In contrast to the spatial…