Journal of Econometrics最新文献_第9页

One instrument to rule them all: The bias and coverage of just-ID IV 一器定乾坤：公正身份证IV的偏差和覆盖面

IF 6.3 3区经济学

Journal of Econometrics Pub Date : 2024-03-01 DOI: 10.1016/j.jeconom.2022.12.012

Joshua Angrist , Michal Kolesár

{"title":"One instrument to rule them all: The bias and coverage of just-ID IV","authors":"Joshua Angrist , Michal Kolesár","doi":"10.1016/j.jeconom.2022.12.012","DOIUrl":"10.1016/j.jeconom.2022.12.012","url":null,"abstract":"<div>We revisit the finite-sample behavior of single-variable just-identified instrumental variables (just-ID IV) estimators, arguing that in most microeconometric applications, the usual inference strategies are likely reliable. Three widely-cited applications are used to explain why this is so. We then consider pretesting strategies of the form <math><mrow><msub><mrow><mi>t</mi></mrow><mrow><mn>1</mn></mrow></msub><mo>></mo><mi>c</mi></mrow></math>, where <math><msub><mrow><mi>t</mi></mrow><mrow><mn>1</mn></mrow></msub></math> is the first-stage <math><mi>t</mi></math>-statistic, and the first-stage sign is given. Although pervasive in empirical practice, pretesting on the first-stage <math><mi>F</mi></math>-statistic exacerbates bias and distorts inference. We show, however, that median bias is both minimized and roughly halved by setting <math><mrow><mi>c</mi><mo>=</mo><mn>0</mn></mrow></math>, that is by screening on the sign of the estimated first stage. This bias reduction is a free lunch: conventional confidence interval coverage is unchanged by screening on the estimated first-stage sign. To the extent that IV analysts sign-screen already, these results strengthen the case for a sanguine view of the finite-sample behavior of just-ID IV.</div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"240 2","pages":"Article 105398"},"PeriodicalIF":6.3,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135753445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Whitney Newey’s contributions to econometrics 惠特尼-纽威对计量经济学的贡献

IF 6.3 3区经济学

Journal of Econometrics Pub Date : 2024-03-01 DOI: 10.1016/j.jeconom.2024.105688

Alberto Abadie , Joshua Angrist , Guido Imbens

引用次数: 0

Network and panel quantile effects via distribution regression 通过分布回归实现网络和面板量化效应

IF 6.3 3区经济学

Journal of Econometrics Pub Date : 2024-03-01 DOI: 10.1016/j.jeconom.2020.08.009

Victor Chernozhukov , Iván Fernández-Val , Martin Weidner

引用次数: 0

Kernel density estimation for undirected dyadic data 无向二元数据的核密度估计

IF 6.3 3区经济学

Journal of Econometrics Pub Date : 2024-03-01 DOI: 10.1016/j.jeconom.2022.06.011

Bryan S. Graham , Fengshi Niu , James L. Powell

{"title":"Kernel density estimation for undirected dyadic data","authors":"Bryan S. Graham , Fengshi Niu , James L. Powell","doi":"10.1016/j.jeconom.2022.06.011","DOIUrl":"https://doi.org/10.1016/j.jeconom.2022.06.011","url":null,"abstract":"<div>We study nonparametric estimation of density functions for undirected dyadic random variables (i.e., random variables defined for all <math><mrow><mi>n</mi><mover><mrow><mo>≡</mo></mrow><mrow><mi>d</mi><mi>e</mi><mi>f</mi></mrow></mover><mfenced><mfrac><mrow><mi>N</mi></mrow><mrow><mn>2</mn></mrow></mfrac></mfenced></mrow></math> unordered pairs of agents/nodes in a weighted network of order <math><mi>N</mi></math>). These random variables satisfy a local dependence property: any random variables in the network that share one or two indices may be dependent, while those sharing no indices in common are independent. In this setting, we show that density functions may be estimated by an application of the kernel estimation method of Rosenblatt (1956) and Parzen (1962). We suggest an estimate of their asymptotic variances inspired by a combination of (i) Newey’s (1994) method of variance estimation for kernel estimators in the “monadic” setting and (ii) a variance estimator for the (estimated) density of a simple network first suggested by Holland and Leinhardt (1976). More unusual are the rates of convergence and asymptotic (normal) distributions of our dyadic density estimates. Specifically, we show that they converge at the same rate as the (unconditional) dyadic sample mean: the square root of the number, <math><mi>N</mi></math>, of nodes. This differs from the results for nonparametric estimation of densities and regression functions for monadic data, which generally have a slower rate of convergence than their corresponding sample mean.</div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"240 2","pages":"Article 105336"},"PeriodicalIF":6.3,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140145459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A comparison of the GB2 and skewed generalized log-t distributions with an application in finance GB2和偏态广义log-t分布在金融中的应用比较

IF 6.3 3区经济学

Journal of Econometrics Pub Date : 2024-03-01 DOI: 10.1016/j.jeconom.2021.01.003

Joshua D. Higbee , James B. McDonald

引用次数: 0

Instrumental variable estimation with first-stage heterogeneity 第一阶段异质性的工具变量估计

IF 6.3 3区经济学

Journal of Econometrics Pub Date : 2024-03-01 DOI: 10.1016/j.jeconom.2023.02.005

Alberto Abadie , Jiaying Gu , Shu Shen

引用次数: 0

Local regression distribution estimators 局部回归分布估计量

IF 6.3 3区经济学

Journal of Econometrics Pub Date : 2024-03-01 DOI: 10.1016/j.jeconom.2021.01.006

Matias D. Cattaneo , Michael Jansson , Xinwei Ma

{"title":"Local regression distribution estimators","authors":"Matias D. Cattaneo , Michael Jansson , Xinwei Ma","doi":"10.1016/j.jeconom.2021.01.006","DOIUrl":"10.1016/j.jeconom.2021.01.006","url":null,"abstract":"<div>This paper investigates the large sample properties of local regression distribution estimators, which include a class of boundary adaptive density estimators as a prime example. First, we establish a pointwise Gaussian large sample distributional approximation in a unified way, allowing for both boundary and interior evaluation points simultaneously. Using this result, we study the asymptotic efficiency of the estimators, and show that a carefully crafted minimum distance implementation based on “redundant” regressors can lead to efficiency gains. Second, we establish uniform linearizations and strong approximations for the estimators, and employ these results to construct valid confidence bands. Third, we develop extensions to weighted distributions with estimated weights and to local <math><msup><mrow><mi>L</mi></mrow><mrow><mn>2</mn></mrow></msup></math> estimation. Finally, we illustrate our methods with two applications in program evaluation: counterfactual density testing, and IV specification and heterogeneity density analysis. Companion software packages in Stata and R are available.</div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"240 2","pages":"Article 105074"},"PeriodicalIF":6.3,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48826557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Using Wasserstein Generative Adversarial Networks for the design of Monte Carlo simulations 使用瓦瑟斯坦生成对抗网络设计蒙特卡罗模拟

IF 6.3 3区经济学

Journal of Econometrics Pub Date : 2024-03-01 DOI: 10.1016/j.jeconom.2020.09.013

Susan Athey , Guido W. Imbens , Jonas Metzger , Evan Munro

{"title":"Using Wasserstein Generative Adversarial Networks for the design of Monte Carlo simulations","authors":"Susan Athey , Guido W. Imbens , Jonas Metzger , Evan Munro","doi":"10.1016/j.jeconom.2020.09.013","DOIUrl":"https://doi.org/10.1016/j.jeconom.2020.09.013","url":null,"abstract":"<div>When researchers develop new econometric methods it is common practice to compare the performance of the new methods to those of existing methods in Monte Carlo studies. The credibility of such Monte Carlo studies is often limited because of the discretion the researcher has in choosing the Monte Carlo designs reported. To improve the credibility we propose using a class of generative models that has recently been developed in the machine learning literature, termed Generative Adversarial Networks (GANs) which can be used to systematically generate artificial data that closely mimics existing datasets. Thus, in combination with existing real data sets, GANs can be used to limit the degrees of freedom in Monte Carlo study designs for the researcher, making any comparisons more convincing. In addition, if an applied researcher is concerned with the performance of a particular statistical method on a specific data set (beyond its theoretical properties in large samples), she can use such GANs to assess the performance of the proposed method, e.g. the coverage rate of confidence intervals or the bias of the estimator, using simulated data which closely resembles the exact setting of interest. To illustrate these methods we apply Wasserstein GANs (WGANs) to the estimation of average treatment effects. In this example, we find that <math><mrow><mo>(</mo><mi>i</mi><mo>)</mo></mrow></math> there is not a single estimator that outperforms the others in all three settings, so researchers should tailor their analytic approach to a given setting, <math><mrow><mo>(</mo><mi>i</mi><mi>i</mi><mo>)</mo></mrow></math> systematic simulation studies can be helpful for selecting among competing methods in this situation, and <math><mrow><mo>(</mo><mi>i</mi><mi>i</mi><mi>i</mi><mo>)</mo></mrow></math> the generated data closely resemble the actual data.</div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"240 2","pages":"Article 105076"},"PeriodicalIF":6.3,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140145357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On uniform inference in nonlinear models with endogeneity 关于具有内生性的非线性模型中的统一推理

IF 6.3 3区经济学

Journal of Econometrics Pub Date : 2024-03-01 DOI: 10.1016/j.jeconom.2021.07.016

Shakeeb Khan , Denis Nekipelov

{"title":"On uniform inference in nonlinear models with endogeneity","authors":"Shakeeb Khan , Denis Nekipelov","doi":"10.1016/j.jeconom.2021.07.016","DOIUrl":"10.1016/j.jeconom.2021.07.016","url":null,"abstract":"<div>This paper explores the uniformity of inference for parameters of interest in nonlinear econometric models with endogeneity. Here the notion of uniformity arises because the behavior of estimators of parameters of interest is shown to vary with where either they or nuisance parameters lie in the parameter space. As a result, inference becomes nonstandard in a fashion that is loosely analogous to inference complications found in the unit root and weak instruments literature, as well as the models recently studied in Andrews and Cheng (2012), Chen et al. (2014), Han and McCloskey (2019). Our main illustrative example is the standard sample selection model, where the parameter of interest is the intercept term as in Heckman (1990), Andrews and Schafgans (1998) and Lewbel (2007). We show here there is a discontinuity in the limiting distribution for an estimator of this parameter despite it being uniformly consistent. This discontinuity prevents standard inference procedures from being valid, and motivates the development of new methods, for which we establish asymptotic properties. Finite sample properties of the procedure are explored through a simulation study and an empirical illustration using the Mroz (1987) data set as in Newey, Powell, and Walker (1990).</div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"240 2","pages":"Article 105261"},"PeriodicalIF":6.3,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132163820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Testing underidentification in linear models, with applications to dynamic panel and asset pricing models 在线性模型中测试欠识别，并应用于动态面板和资产定价模型

IF 6.3 3区经济学

Journal of Econometrics Pub Date : 2024-03-01 DOI: 10.1016/j.jeconom.2021.03.007

Frank Windmeijer

{"title":"Testing underidentification in linear models, with applications to dynamic panel and asset pricing models","authors":"Frank Windmeijer","doi":"10.1016/j.jeconom.2021.03.007","DOIUrl":"10.1016/j.jeconom.2021.03.007","url":null,"abstract":"<div>This paper develops the links between overidentification tests, underidentification tests, score tests and the Cragg and Donald (1993, 1997) and Kleibergen and Paap (2006) rank tests in linear instrumental variable (IV) models. For the structural linear model <math><mrow><mi>y</mi><mo>=</mo><mi>X</mi><mi>β</mi><mo>+</mo><mi>u</mi></mrow></math>, with the endogenous explanatory variables partitioned as <math><mrow><mi>X</mi><mo>=</mo><mfenced><mrow><msub><mrow><mi>x</mi></mrow><mrow><mn>1</mn></mrow></msub><mspace></mspace><msub><mrow><mi>X</mi></mrow><mrow><mn>2</mn></mrow></msub></mrow></mfenced></mrow></math>, this general framework shows that standard underidentification tests are tests for overidentification in an auxiliary linear model, <math><mrow><msub><mrow><mi>x</mi></mrow><mrow><mn>1</mn></mrow></msub><mo>=</mo><msub><mrow><mi>X</mi></mrow><mrow><mn>2</mn></mrow></msub><mi>δ</mi><mo>+</mo><mi>ɛ</mi></mrow></math>, estimated by IV estimation methods using the same instruments as for the original model. This simple structure makes it possible to establish valid robust underidentification tests for linear IV models where these have not been proposed or used before, like clustered dynamic panel data models estimated by GMM. The framework also applies to tests for the rank of general parameter matrices. Invariant rank tests are based on the LIML or continuously updated GMM estimators of both structural and first-stage parameters. This insight leads to the proposal of new two-step invariant asymptotically efficient GMM estimators, and a new iterated GMM estimator that, if it converges, converges to the continuously updated GMM estimator.</div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"240 2","pages":"Article 105104"},"PeriodicalIF":6.3,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43824339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0