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The Finite Sample Performance of Inference Methods for Propensity Score Matching and Weighting Estimators

倾向得分匹配与加权估计量推断方法的有限样本表现

This article investigates the finite sample properties of a range of inference methods for propensity score-based matching and weighting estimators frequently applied to evaluate the average treatment effect on the treated. We analyze both asymptotic approximations and bootstrap methods for computing variances and confidence intervals in our simulation designs, which are based on German register data and U.S. survey data. We vary the design w.r.t. treatment selectivity,…

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Shape-Constrained Kernel-Weighted Least Squares: Estimating Production Functions for Chilean Manufacturing Industries

形状约束核加权最小二乘:智利制造业生产函数估计

In this article, we examine a novel way of imposing shape constraints on a local polynomial kernel estimator. The proposed approach is referred to as shape constrained kernel-weighted least squares (SCKLS). We prove uniform consistency of the SCKLS estimator with monotonicity and convexity/concavity constraints and establish its convergence rate. In addition, we propose a test to validate whether shape constraints are correctly specified. The competitiveness of SCKLS is…

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The Estimation of Compensating Wage Differentials: Lessons From the Deadliest Catch

补偿性工资差异的估计:来自高致死风险渔业的启示

I use longitudinal survey data from commercial fishing deckhands in the Alaskan Bering Sea to provide new insights on empirical methods commonly used to estimate compensating wage differentials and the value of statistical life (VSL). The unique setting exploits intertemporal variation in fatality rates and wages within worker-vessel pairs caused by a combination of weather patterns and policy changes, allowing identification of parameters and biases that it has only been…

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Optimal data collection for randomized control trials

随机对照试验的最优数据收集

Summary In a randomized control trial, the precision of an average treatment effect estimator and the power of the corresponding t-test can be improved either by collecting data on additional individuals, or by collecting additional covariates that predict the outcome variable. To design the experiment, a researcher needs to solve this trade-off subject to her budget constraint. We show that this optimization problem is equivalent to optimally predicting outcomes by the…

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Kernel estimation for panel data with heterogeneous dynamics

异质动态面板数据的核估计

Summary This paper proposes nonparametric kernel-smoothing estimation for panel data to examine the degree of heterogeneity across cross-sectional units. We first estimate the sample mean, autocovariances, and autocorrelations for each unit and then apply kernel smoothing to compute their density functions. The dependence of the kernel estimator on bandwidth makes asymptotic bias of very high order affect the required condition on the relative magnitudes of the…

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A new structural break test for panels with common factors

含共同因子面板的一种新结构突变检验

Summary This paper develops new tests against a structural break in panel data models with common factors when T is fixed, where T denotes the number of observations over time. For this class of models, the available tests against a structural break are valid only under the assumption that T is ‘large’. However, this may be a stringent requirement—more commonly so in datasets with annual time frequency, in which case the sample may cover a relatively long period even if T is…

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Information technology outsourcing and firm productivity: eliminating bias from selective missingness in the dependent variable

信息技术外包与企业生产率:消除因变量选择性缺失带来的偏差

Summary Missing values are a major problem in all econometric applications based on survey data. A standard approach assumes data are missing at random and uses imputation methods or even listwise deletion. This approach is justified if item nonresponse does not depend on the potentially missing variables’ realization. However, assuming missingness at random may introduce bias if nonresponse is, in fact, selective. Relevant applications range from financial or strategic…

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Inference on finite-population treatment effects under limited overlap

重叠有限时有限总体处理效应的推断

Summary This paper studies inference on finite-population average and local average treatment effects under limited overlap, meaning that some strata have a small proportion of treated or untreated units. We model limited overlap in an asymptotic framework, sending the propensity score to zero (or one) with the sample size. We derive the asymptotic distribution of analogue estimators of the treatment effects under two common randomization schemes: conditionally independent…

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Initial conditions of dynamic panel data models: on within and between equations

动态面板模型的初始条件:组内方程与组间方程

Summary This paper investigates the quasi-maximum likelihood estimation of short dynamic panel data models. We consider their estimation on both fixed effects and random effects specifications and propose a Hausman test when exogenous variables are present. For a dynamic panel model, initial conditions play important roles in model structure and estimation, and they give rise to a between equation under the random effects framework. With the between equation properly…

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Cognitive and Noncognitive Costs of Day Care at Age 0–2 for Children in Advantaged Families

优势家庭儿童0—2岁日托的认知与非认知代价

Exploiting admission thresholds to the Bologna day care system, we show using a regression discontinuity (RD) design that one additional day care month at age 0–2 reduces intelligence quotient by 0.5% (4.7% of a standard deviation) at age 8–14 in a relatively affluent population. The magnitude of this negative effect increases with family income. Similar negative impacts are found for personality traits. These findings are consistent with the hypothesis from psychology that…

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A Comment on: “On the Informativeness of Descriptive Statistics for Structural Estimates” by Isaiah Andrews, Matthew Gentzkow, and Jesse M. Shapiro

对《描述性统计对结构估计的信息含量》的评论

LET ME FIRST BRIEFLY RECALL the bias expressions in the paper.For simplicity, throughout I will fix a value η of the base model parameter.Let f η ζ be the density of the data D under the reader's model.Let f η denote the density of D under the base model.Given a quantity of interest c(η), the unrestricted bias of c is

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A Comment on: “On the Informativeness of Descriptive Statistics for Structural Estimates” by Isaiah Andrews, Matthew Gentzkow, and Jesse M. Shapiro

对《描述性统计对结构估计的信息含量》的评论

ANDREWS, GENTZKOW, AND SHAPIRO (2020), henceforth AGS, make important progress on a challenging and fundamental question. The authors consider a setting in which a researcher reports an estimate ĉn that is unbiased under the researcher’s model. The researcher also reports a statistic γ̂n and a measure of informativeness of γ̂n for ĉn, denoted by . AGS show can be related to properties of the distribution of ĉn under models larger than the one employed by the researcher. As a…

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On the Informativeness of Descriptive Statistics for Structural Estimates

描述性统计对结构估计的信息含量

We propose a way to formalize the relationship between descriptive analysis and structural estimation. A researcher reports an estimate ĉ of a structural quantity of interest c that is exactly or asymptotically unbiased under some base model. The researcher also reports descriptive statistics γ ˆ that estimate features γ of the distribution of the data that are related to c under the base model. A reader entertains a less restrictive model that is local to the base model,…

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Frontier Culture: The Roots and Persistence of “Rugged Individualism” in the United States

边疆文化:美国“强烈个人主义”的根源与延续

The presence of a westward‐moving frontier of settlement shaped early U.S. history. In 1893, the historian Frederick Jackson Turner famously argued that the American frontier fostered individualism. We investigate the “frontier thesis” and identify its long‐run implications for culture and politics. We track the frontier throughout the 1790–1890 period and construct a novel, county‐level measure of total frontier experience (TFE). Historically, frontier locations had…

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Leave‐Out Estimation of Variance Components

We propose leave‐out estimators of quadratic forms designed for the study of linear models with unrestricted heteroscedasticity. Applications include analysis of variance and tests of linear restrictions in models with many regressors. An approximation algorithm is provided that enables accurate computation of the estimator in very large data sets. We study the large sample properties of our estimator allowing the number of regressors to grow in proportion to the number of…

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Dynamic Spatial Panel Models: Networks, Common Shocks, and Sequential Exogeneity

This paper considers a class of generalized methods of moments (GMM) estimators for general dynamic panel models, allowing for weakly exogenous covariates and cross‐sectional dependence due to spatial lags, unspecified common shocks, and time‐varying interactive effects. We significantly expand the scope of the existing literature by allowing for endogenous time‐varying spatial weight matrices without imposing explicit structural assumptions on how the weights are formed. An…

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A Structural Econometric Analysis of Network Formation Games Through Subnetworks

The objective of this paper is to identify and estimate network formation models using observed data on network structure. We characterize network formation as a simultaneous‐move game, where the utility from forming a link depends on the structure of the network, thereby generating strategic interactions between links. With the prevalence of multiple equilibria, the parameters are not necessarily point identified. We leave the equilibrium selection unrestricted and propose…

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Statistical Inference in Games

We consider statistical inference in games. Each player obtains a small random sample of other players' actions, uses statistical inference to estimate their actions, and chooses an optimal action based on the estimate. In a sampling equilibrium with statistical inference (SESI), the sample is drawn from the distribution of players' actions based on this process. We characterize the set of SESIs in large two‐action games, and compare their predictions to those of Nash…

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Estimating the Effect of Treatments Allocated by Randomized Waiting Lists

Oversubscribed treatments are often allocated using randomized waiting lists. Applicants are ranked randomly, and treatment offers are made following that ranking until all seats are filled. To estimate causal effects, researchers often compare applicants getting and not getting an offer. We show that those two groups are not statistically comparable. Therefore, the estimator arising from that comparison is inconsistent when the number of waitlists goes to infinity. We…

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Selection Without Exclusion

It is well understood that classical sample selection models are not semiparametrically identified without exclusion restrictions. Lee (2009) developed bounds for the parameters in a model that nests the semiparametric sample selection model. These bounds can be wide. In this paper, we investigate bounds that impose the full structure of a sample selection model with errors that are independent of the explanatory variables but have unknown distribution. The additional…

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Estimation of a Roy/Search/Compensating Differential Model of the Labor Market

劳动力市场中Roy模型、搜寻与补偿性工资差异的综合模型估计

In this paper, we develop a model that captures key components of the Roy model, a search model, compensating differentials, and human capital accumulation on‐the‐job. We establish which components of the model can be non‐parametrically identified and which ones cannot. We estimate the model and use it to assess the relative contribution of the different factors for overall wage inequality. We find that variation in premarket skills (the key feature of the Roy model) is the…

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Sampling‐Based versus Design‐Based Uncertainty in Regression Analysis

回归分析中基于抽样与基于设计的不确定性

Consider a researcher estimating the parameters of a regression function based on data for all 50 states in the United States or on data for all visits to a website. What is the interpretation of the estimated parameters and the standard errors? In practice, researchers typically assume that the sample is randomly drawn from a large population of interest and report standard errors that are designed to capture sampling variation. This is common even in applications where it…

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