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回归Copula的贝叶斯推断AI 解读

We propose a new semiparametric distributional regression smoother that is based on a copula decomposition of the joint distribution of the vector of response values. The copula is high-dimensional and constructed by inversion of a pseudo regression, where the conditional mean and variance are semiparametric functions of covariates modeled using regularized basis functions. By integrating out the basis coefficients, an implicit copula process on the covariate space is…

具有非平稳性与部分可观测因子结构的变系数面板数据模型AI 解读

In this article, we study a varying-coefficient panel data model with both nonstationarity and partially observed factor structure. Two approaches are proposed. The first approach proposed in the main text considers a sieve based method to estimate the unknown coefficients as well as the factors and loading functions simultaneously, while the second approach proposed in the online supplementary document involving the principal component analysis provides an alternative…

基于潜在结果与贝叶斯推断的品类及品牌购买动态两阶段模型AI 解读

We propose an econometric two-stage model for category-level purchase and brand-level purchase that allows for simultaneous brand purchases in the analysis of scanner panel data. The proposed model formulation is consistent with the traditional theory of consumer behavior. We conduct Bayesian estimation with the Markov chain Monte Carlo algorithm for our proposed model. The simulation studies show that previously proposed related models can cause severe bias in predicting…

投资激增之后：投资事件与企业绩效AI 解读

Our study aims at investigating the relationship between investment spikes and subsequent productivity development at the firm level. We propose a novel identification scheme for the effects of an investment spike, using matching techniques and a tailored econometric modeling. It allows us to find efficiency differentials against matched firms in periods adjacent to the spike. We showed that TFP persistently falls after an investment spike, which is consistent with…

提前规划能否改善社区？来自坦桑尼亚的长期证据AI 解读

Africa’s demand for urban housing is soaring, even as it faces a proliferation of slums. In this setting, can modest infrastructure investments in greenfield areas where people subsequently build their own houses facilitate long-run neighborhood development? We study Sites and Services projects implemented in seven Tanzanian cities during the 1970s and 1980s, and we use a spatial regression discontinuity design to compare greenfield areas that were treated (de novo) with…

内生二元处理下适用于任意响应类型的工具残差估计AI 解读

Abstract Given an endogenous/confounded binary treatment D, a response Y with its potential versions (Y0, Y1) and covariates X, finding the treatment effect is difficult if Y is not continuous, even when a binary instrumental variable (IV) Z is available. We show that, for any form of Y (continuous, binary, mixed,…), there exists a decomposition Y = μ0(X) + μ1(X)D + error with E(error|Z,X) = 0, where μ1(X)≡E(Y1-Y0|complier,X) and ‘compliers’ are those who get treated if and…

二元工具变量与删失数据下的因果分位数效应估计AI 解读

Abstract The causal effect of a treatment is of fundamental interest in the social, biological and health sciences. Instrumental variable (IV) methods are commonly used to determine causal treatment effects in the presence of unmeasured confounding. In this work, we study a new binary IV framework with randomly censored outcomes where we propose to quantify the causal treatment effect by the concept of complier quantile causal effect (CQCE). The CQCE is identifiable under…

提高观察性研究中异常结局检验功效的自适应方法AI 解读

Abstract In many observational studies, the interest is in the effect of treatment on bad, aberrant outcomes rather than the average outcome. For such settings, the traditional approach is to define a dichotomous outcome indicating aberration from a continuous score and use the Mantel–Haenszel test with matched data. For example, studies of determinants of poor child growth use the World Health Organization’s definition of child stunting being height-for-age z-score ≤ − 2.…

类别解释变量的收缩估计AI 解读

This paper introduces a flexible regularization approach that reduces point estimation risk of group means stemming from e.g. categorical regressors, (quasi-)experimental data or panel data models. The loss function is penalized by adding weighted squared ℓ2-norm differences between group location parameters and informative first stage estimates. Under quadratic loss, the penalized estimation problem has a simple interpretable closed-form solution that nests methods…

横截面与时间依赖下大面板的无平滑参数推断AI 解读

This paper addresses inference in large panel data models in the presence of both cross-sectional and temporal dependence of unknown form. We are interested in making inferences that do not rely on the choice of any smoothing parameter as is the case with the often employed “HAC” estimator for the covariance matrix. To that end, we propose a cluster estimator for the asymptotic covariance of the estimators and valid bootstrap schemes that do not require the selection of a…

含未观测效应的非线性面板模型的积分似然推断AI 解读

We propose a new integrated likelihood based approach for estimating panel data models when the unobserved individual effects enter the model nonlinearly. Unlike existing integrated likelihoods in the literature, the one we propose is closer to a genuine likelihood. Although the statistical theory for the proposed estimator is developed in an asymptotic setting where the number of individuals and the number of time periods both approach infinity, results from a simulation…

以实验者满意化决策模型指导受损实验的有限样本推断AI 解读

Summary This paper presents a simple decision-theoretic economic approach for analysing social experiments with compromised random assignment protocols that are only partially documented. We model administratively constrained experimenters who satisfice in seeking covariate balance. We develop design-based small-sample hypothesis tests that use worst-case (least favourable) randomization null distributions. Our approach accommodates a variety of compromised experiments,…

简单二元结果面板数据模型中的识别AI 解读

Summary This paper first reviews some of the approaches that have been taken to estimate the common parameters of binary outcome models with fixed effects. We limit attention to situations in which the researcher has access to a data set with a large number of units (individuals or companies, for example) observed over a number of time periods. We then apply some of the existing approaches to study fixed-effects panel data versions of entry games, like the ones studied in…

非可分面板模型的低秩近似AI 解读

Summary We provide estimation methods for nonseparable panel models based on low-rank factor structure approximations. The factor structures are estimated by matrix-completion methods to deal with the computational challenges of principal component analysis in the presence of missing data. We show that the resulting estimators are consistent in large panels, but suffer from approximation and shrinkage biases. We correct these biases using matching and…

条件平均处理效应及其他因果函数的去偏机器学习AI 解读

Summary This paper provides estimation and inference methods for the best linear predictor (approximation) of a structural function, such as conditional average structural and treatment effects, and structural derivatives, based on modern machine learning tools. We represent this structural function as a conditional expectation of an unbiased signal that depends on a nuisance parameter, which we estimate by modern machine learning techniques. We first adjust the signal to…

基于平滑分位数回归的简便面板分位数模型估计量AI 解读

Summary This paper considers panel data models where the idiosyncratic errors are subject to conditonal quantile restrictions. We propose a two-step estimator based on smoothed quantile regressions that is easy to implement. The asymptotic distribution of the estimator is established, and the analytical expression of its asymptotic bias is derived. Building on these results, we show how to make asymptotically valid inference on the basis of both analytical and split-panel…

重尾模型中的因果发现AI 解读

Causal questions are omnipresent in many scientific problems. While much progress has been made in the analysis of causal relationships between random variables, these methods are not well suited if the causal mechanisms only manifest themselves in extremes. This work aims to connect the two fields of causal inference and extreme value theory. We define the causal tail coefficient that captures asymmetries in the extremal dependence of two random variables. In the population…

因果自助法AI 解读

The bootstrap, introduced by The Jackknife, the Bootstrap and Other Resampling Plans ((1982), SIAM), has become a very popular method for estimating variances and constructing confidence intervals. A key insight is that one can approximate the properties of estimators by using the empirical distribution function of the sample as an approximation for the true distribution function. This approach views the uncertainty in the estimator as coming exclusively from sampling…

不规则识别与大量极端倾向得分下处理效应的有效推断AI 解读

This paper provides a framework for conducting valid inference for causal parameters without imposing strong variance or support restrictions on the propensity score. In particular, it covers the case of irregularly identified treatment effect parameters. We provide limit theorems for inverse probability weighting and doubly robust estimation of causal or counterfactual parameters that do not rely on trimming approaches. By construction the limiting distributions of these…

工具变量模型检验中真实与名义拒绝概率差的界AI 解读

This paper presents a simple method for carrying out inference in a wide variety of possibly nonlinear IV models under weak assumptions. The method provides a finite-sample bound on the difference between the true and nominal probabilities of rejecting a correct null hypothesis. The method is a non-Studentized version of the S test of Stock and Wright (2000) but is implemented and analyzed differently. It does not require restrictive distributional assumptions, linearity of…

识别二元选择中的异质性社会效应AI 解读

We identify and estimate heterogeneous social effects within groups of individuals that make binary choices. These heterogeneous social effects, which include peer and contextual effects, are modeled through unobserved influence matrices that summarize how the members within each group affect each other’s outcomes. We recover parameters in social effects as well as the unknown influence matrices by exploiting how these matrices are linked to the reduced-form effects of…

网络依赖随机变量的极限定理AI 解读

This paper is concerned with cross-sectional dependence arising because observations are interconnected through an observed network. Following (Doukhan and Louhichi, 1999), we measure the strength of dependence by covariances of nonlinearly transformed variables. We provide a law of large numbers and central limit theorem for network dependent variables. We also provide a method of calculating standard errors robust to general forms of network dependence. For that purpose,…

利用面板数据界定分布处理效应：失业冲击应用AI 解读

This paper develops new techniques to bound distributional treatment effect parameters that depend on the joint distribution of potential outcomes — an object not identified by standard identifying assumptions such as selection on observables or even when treatment is randomly assigned. I show that panel data and an additional assumption on the dependence between untreated potential outcomes for the treated group over time (i) provide more identifying power for…

随机单调性的自适应检验AI 解读

We propose a new nonparametric test of stochastic monotonicity which adapts to the unknown smoothness of the conditional distribution of interest, possesses desirable asymptotic properties, is conceptually easy to implement, and computationally attractive. In particular, we show that the test asymptotically controls size at a polynomial rate, is nonconservative, and detects certain smooth local alternatives that converge to the null with the fastest possible rate. Our test…