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国家分裂的经济学：评估分离独立的经济影响AI 解读

Abstract This paper presents estimates of the economic effects of secession for a large panel of countries that gained independence between 1940 and 2016. It relies on a semi‐parametric identification strategy that controls for the confounding effects of past gross domestic product (GDP) dynamics, anticipation effects, unobserved heterogeneity, model uncertainty, and effect heterogeneity. Our findings indicate that secession lowered per capita GDP, on average by around 24%…

经济学中的实证策略：照亮从原因到结果的路径AI 解读

The view that empirical strategies in economics should be transparent and credible now goes almost without saying. By revealing for whom particular instrumental variables (IV) estimates are valid, the local average treatment effects (LATE) framework helped make this so. This lecture uses empirical examples, mostly involving effects of charter and exam school attendance, to illustrate the value of the LATE framework for causal inference. LATE distinguishes independence…

为最坏情形做准备、对最好情形抱希望：稳健贝叶斯说服AI 解读

We propose a robust solution concept for Bayesian persuasion that accounts for the Sender's concern that her Bayesian belief about the environment—which we call the conjecture —may be false. Specifically, the Sender is uncertain about the exogenous sources of information the Receivers may learn from, and about strategy selection. She first identifies all information policies that yield the largest payoff in the “worst‐case scenario,” that is, when Nature provides information…

动态分配处理的长期效应：新方法与培训收入效应评估AI 解读

We propose and implement a new method to estimate treatment effects in settings where individuals need to be in a certain state (e.g., unemployment) to be eligible for a treatment, treatments may commence at different points in time, and the outcome of interest is realized after the individual left the initial state. An example concerns the effect of training on earnings in subsequent employment. Any evaluation needs to take into account that some of those who are not…

弱识别 GMM 的最优决策规则AI 解读

This paper studies optimal decision rules, including estimators and tests, for weakly identified GMM models. We derive the limit experiment for weakly identified GMM, and propose a theoretically‐motivated class of priors which give rise to quasi‐Bayes decision rules as a limiting case. Together with results in the previous literature, this establishes desirable properties for the quasi‐Bayes approach regardless of model identification status, and we recommend quasi‐Bayes for…

宏观—金融解耦：宏观资产定价模型的稳健评估AI 解读

This paper shows that robust inference under weak identification is important to the evaluation of many influential macro asset pricing models, including (time‐varying) rare‐disaster risk models and long‐run risk models. Building on recent developments in the conditional inference literature, we provide a novel conditional specification test by simulating the critical value conditional on a sufficient statistic. This sufficient statistic can be intuitively interpreted as a…

利用网络数据识别与估计部分线性回归模型AI 解读

I study a regression model in which one covariate is an unknown function of a latent driver of link formation in a network. Rather than specify and fit a parametric network formation model, I introduce a new method based on matching pairs of agents with similar columns of the squared adjacency matrix, the ij th entry of which contains the number of other agents linked to both agents i and j . The intuition behind this approach is that for a large class of network formation…

最小化对模型误设的敏感性AI 解读

We propose a framework for estimation and inference when the model may be misspecified. We rely on a local asymptotic approach where the degree of misspecification is indexed by the sample size. We construct estimators whose mean squared error is minimax in a neighborhood of the reference model, based on one‐step adjustments. In addition, we provide confidence intervals that contain the true parameter under local misspecification. As a tool to interpret the degree of…

信息不足卖家的秘密保留价AI 解读

If bidders are better informed than the seller about a common component of auction heterogeneity, the seller can allocate more efficiently by keeping her reserve price secret and revising it using submitted bids. We build a model of a first‐price auction under unobserved auction heterogeneity—imperfectly observed by the seller—that captures this rationale and derive conditions for identification. An application to French timber auctions, where such revisions are widely used,…

部分有序备选项的离散选择模型AI 解读

In this paper, we analyze a discrete choice model for partially ordered alternatives. The alternatives are differentiated along two dimensions: the first an unordered “horizontal” dimension, and the second an ordered “vertical” dimension. The model can be used in circumstances in which individuals choose among products of different brands, wherein each brand offers an ordered choice menu, for example, by offering products of varying quality. The unordered–ordered nature of…

升价拍卖的识别及其数字版权管理应用AI 解读

This study provides new identification and estimation results for ascending (traditional English or online) auctions with unobserved auction‐level heterogeneity and an unknown number of bidders. When the seller's reserve price and two order statistics of bids are observed, we derive conditions under which the distributions of buyer valuations, unobserved heterogeneity, and number of participants are point identified. We also derive conditions for point identification in…

识别假设的不确定性AI 解读

Uncertainty about the choice of identifying assumptions is common in causal studies, but is often ignored in empirical practice. This paper considers uncertainty over models that impose different identifying assumptions, which can lead to a mix of point‐ and set‐identified models. We propose performing inference in the presence of such uncertainty by generalizing Bayesian model averaging. The method considers multiple posteriors for the set‐identified models and combines…

非线性半参数模型的正则化正交机器学习AI 解读

Summary This paper proposes a Lasso-type estimator for a high-dimensional sparse parameter identified by a single index conditional moment restriction (CMR). In addition to this parameter, the moment function can also depend on a nuisance function, such as the propensity score or the conditional choice probability, which we estimate by modern machine learning tools. We first adjust the moment function so that the gradient of the future loss function is insensitive (formally,…

通过约束检测选择性与准确性界定感染率：早期新冠疫情应用AI 解读

Summary I propose novel partial identification bounds on infection prevalence from information on test rate and test yield. The approach utilizes user-specified bounds on (i) test accuracy and (ii) the extent to which tests are targeted, formalized as restriction on the effect of true infection status on the odds ratio of getting tested and thereby embeddable in logit specifications. The motivating application is to the COVID-19 pandemic but the strategy may also be useful…

基于正则化刀切工具变量法的多工具变量异方差过度识别检验AI 解读

Summary This paper proposes a new overidentifying restrictions test in a linear model when the number of instruments (possibly weak) may be smaller or larger than the sample size n or even infinite in a heteroscedastic framework. The proposed J test combines two techniques: the jackknife method and the regularisation technique which consists in stabilising the projection matrix. We theoretically show that our new test achieves the asymptotically correct size in the presence…

波动率预测比较中的排序不变条件AI 解读

Summary The paper derives four conditions that guarantee rank-invariance, i.e., that the empirical rankings (based on measurement error-affected variance proxies) of competing volatility forecasts be consistent with the true rankings (based on the unobservable conditional variance). The first three establish bounds beyond which the separation between the forecasts is enough for their rankings not to be affected by the measurement error. The conditions’ ability to establish…

工具变量独立性下潜在结果分布的识别区域AI 解读

This paper examines the identifying power of instrument exogeneity in the treatment effect model. We derive the identification region of the potential outcome distributions, which are the collection of distributions that are compatible with data and with the restrictions of the model. We consider identification when (i) the instrument is independent of each of the potential outcomes (marginal independence), (ii) the instrument is independent of the potential outcomes and…

多时期双重差分的识别、估计与推断AI 解读

In this article, we consider identification, estimation, and inference procedures for treatment effect parameters using Difference-in-Differences (DiD) with (i) multiple time periods, (ii) variation in treatment timing, and (iii) when the “parallel trends assumption” holds potentially only after conditioning on observed covariates. We show that a family of causal effect parameters are identified in staggered DiD setups, even if differences in observed characteristics create…

动态处理效应非参数模型中的识别AI 解读

This paper develops a nonparametric model that represents how sequences of outcomes and treatment choices influence one another in a dynamic manner. In this setting, we are interested in identifying the average outcome for individuals in each period, had a particular treatment sequence been assigned. The identification of this quantity allows us to identify the average treatment effects (ATE’s) and the ATE’s on transitions, as well as the optimal treatment regimes, namely,…

具有不限定分布的相关随机效应的非线性面板模型AI 解读

Under a Mundlak-type correlated random effect (CRE) specification, we first show that the average likelihood of a parametric nonlinear panel data model is the convolution of the conditional distribution of the model and the distribution of the unobserved heterogeneity. Hence, the distribution of the unobserved heterogeneity can be recovered by means of a Fourier transformation without imposing a distributional assumption on the CRE specification. We subsequently construct a…

多重相互依赖持续时间模型AI 解读

We propose a multiplex interdependent durations model and study its empirical content. The model considers an empirical stopping game of multiple agents making optimal timing decisions with incomplete information. We characterize the unique Bayesian Nash equilibrium of the stopping game in a system of simultaneous equations involving the conditional distribution of each duration with a moderate strategic interaction condition. The system of nonlinear simultaneous equations…

二元工具变量模型下局部处理效应的估计AI 解读

Summary Instrumental variables are widely used to deal with unmeasured confounding in observational studies and imperfect randomized controlled trials. In these studies, researchers often target the so-called local average treatment effect as it is identifiable under mild conditions. In this paper we consider estimation of the local average treatment effect under the binary instrumental variable model. We discuss the challenges of causal estimation with a binary outcome and…

连续处理下工具变量有效性的不可检验性AI 解读

Summary This note presents a proof of the conjecture in Pearl (1995) about testing the validity of an instrumental variable in hidden variable models. It implies that instrument validity cannot be tested in the case where the endogenous treatment is continuously distributed. This stands in contrast to the classical testability results for instrument validity when the treatment is discrete. However, imposing weak structural assumptions on the model, such as continuity between…

学习基于 U 统计量的矩阵块结构AI 解读

Summary We introduce a conceptually simple, efficient and easily implemented approach for learning the block structure in a large matrix. Using the properties of U-statistics and large-dimensional random matrix theory, the group structure of many variables can be directly identified based on the eigenvalues and eigenvectors of the scaled sample matrix. We also establish the asymptotic properties of the proposed approach under mild conditions. The finite-sample performance of…