Journal of Business & Economic Statistics最新文献

A Ridge-Regularized Jackknifed Anderson-Rubin Test. 山脊-规则化 Jackknifed Anderson-Rubin 试验。

IF 3 2区数学

Journal of Business & Economic Statistics Pub Date : 2023-12-12 eCollection Date: 2024-01-01 DOI: 10.1080/07350015.2023.2290739

Max-Sebastian Dovì, Anders Bredahl Kock, Sophocles Mavroeidis

引用次数: 0

Efficient and Robust Estimation of the Generalized LATE Model 广义LATE模型的高效鲁棒估计

2区数学

Journal of Business & Economic Statistics Pub Date : 2023-11-14 DOI: 10.1080/07350015.2023.2282497

Haitian Xie

{"title":"Efficient and Robust Estimation of the Generalized LATE Model","authors":"Haitian Xie","doi":"10.1080/07350015.2023.2282497","DOIUrl":"https://doi.org/10.1080/07350015.2023.2282497","url":null,"abstract":"Abstract–This paper studies the estimation of causal parameters in the generalized local average treatment effect (GLATE) model, which expands upon the traditional LATE model to include multivalued treatments. We derive the efficient influence function (EIF) and the semiparametric efficiency bound (SPEB) for two types of causal parameters: the local average structural function (LASF) and the local average structural function for the treated (LASFT). The moment conditions generated by the EIF satisfy two robustness properties: double robustness and Neyman orthogonality. Based on the robust moment conditions, we propose the double/debiased machine learning (DML) estimator for estimating the LASF. The DML estimator is well-suited for high dimensional settings. We also propose null-restricted inference methods that are robust against weak identification issues. As an empirical application of these methods, we examine the potential health outcome across different types of health insurance plans using data from the Oregon Health Insurance Experiment.Keywords: Double RobustnessEfficient Influence FunctionMultivalued TreatmentNeyman OrthogonalityUnordered MonotonicityWeak Identification.DisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":"53 46","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134902481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Causal inference under outcome-based sampling with monotonicity assumptions 基于结果抽样的单调性假设下的因果推理

2区数学

Journal of Business & Economic Statistics Pub Date : 2023-10-31 DOI: 10.1080/07350015.2023.2277164

Sung Jae Jun, Sokbae Lee

{"title":"Causal inference under outcome-based sampling with monotonicity assumptions","authors":"Sung Jae Jun, Sokbae Lee","doi":"10.1080/07350015.2023.2277164","DOIUrl":"https://doi.org/10.1080/07350015.2023.2277164","url":null,"abstract":"We study causal inference under case-control and case-population sampling. For this purpose, we focus on the binary-outcome and binary-treatment case, where the parameters of interest are causal relative and attributable risk defined via the potential outcome framework. It is shown that strong ignorability is not always as powerful as it is under random sampling and that certain monotonicity assumptions yield comparable results in terms of sharp identified intervals. Specifically, the usual odds ratio is shown to be a sharp identified upper bound on causal relative risk under the monotone treatment response and monotone treatment selection assumptions. We then discuss averaging the conditional (log) odds ratio and propose an algorithm for semiparametrically efficient estimation when averaging is based only on the (conditional) distributions of the covariates that are identified in the data. We also offer algorithms for causal inference if the true population distribution of the covariates is desirable for aggregation. We show the usefulness of our approach by studying two empirical examples from social sciences: the benefit of attending private school for entering a prestigious university in Pakistan and the causal relationship between staying in school and getting involved with drug-trafficking gangs in Brazil.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":"50 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135765836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

An Empirical Bayes Approach to Controlling the False Discovery Exceedance 控制虚假发现超越的经验贝叶斯方法

2区数学

Journal of Business & Economic Statistics Pub Date : 2023-10-31 DOI: 10.1080/07350015.2023.2277857

Pallavi Basu, Luella Fu, Alessio Saretto, Wenguang Sun

引用次数: 0

Modeling and Forecasting Macroeconomic Downside Risk* 宏观经济下行风险建模与预测*

2区数学

Journal of Business & Economic Statistics Pub Date : 2023-10-31 DOI: 10.1080/07350015.2023.2277171

Davide Delle Monache, Andrea De Polis, Ivan Petrella

{"title":"Modeling and Forecasting Macroeconomic Downside Risk*","authors":"Davide Delle Monache, Andrea De Polis, Ivan Petrella","doi":"10.1080/07350015.2023.2277171","DOIUrl":"https://doi.org/10.1080/07350015.2023.2277171","url":null,"abstract":"AbstractWe model permanent and transitory changes of the predictive density of US GDP growth. A substantial increase in downside risk to US economic growth emerges over the last 30 years, associated with the long-run growth slowdown started in the early 2000s. Conditional skewness moves procyclically, implying negatively skewed predictive densities ahead and during recessions, often anticipated by deteriorating financial conditions. Conversely, positively skewed distributions characterize expansions. The modelling framework ensures robustness to tail events, allows for both dense or sparse predictor designs, and delivers competitive out-of-sample (point, density and tail) forecasts, improving upon standard benchmarks.Keywords: Business cycledownside riskskewnessscore drivenfinancial conditionsDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":"465 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135764630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Functional-Coefficient Quantile Regression for Panel Data with Latent Group Structure 具有潜在群体结构的面板数据的功能系数分位数回归

2区数学

Journal of Business & Economic Statistics Pub Date : 2023-10-31 DOI: 10.1080/07350015.2023.2277172

Xiaorong Yang, Jia Chen, Degui Li, Runze Li

{"title":"Functional-Coefficient Quantile Regression for Panel Data with Latent Group Structure","authors":"Xiaorong Yang, Jia Chen, Degui Li, Runze Li","doi":"10.1080/07350015.2023.2277172","DOIUrl":"https://doi.org/10.1080/07350015.2023.2277172","url":null,"abstract":"AbstractThis paper considers estimating functional-coefficient models in panel quantile regression with individual effects, allowing the cross-sectional and temporal dependence for large panel observations. A latent group structure is imposed on the heterogenous quantile regression models so that the number of nonparametric functional coefficients to be estimated can be reduced considerably. With the preliminary local linear quantile estimates of the subject-specific functional coefficients, a classic agglomerative clustering algorithm is used to estimate the unknown group structure and an easy-to-implement ratio criterion is proposed to determine the group number. The estimated group number and structure are shown to be consistent. Furthermore, a post-grouping local linear smoothing method is introduced to estimate the group-specific functional coefficients, and the relevant asymptotic normal distribution theory is derived with a normalisation rate comparable to that in the literature. The developed methodologies and theory are verified through a simulation study and showcased with an application to house price data from UK local authority districts, which reveals different homogeneity structures at different quantile levels.Keywords: Cluster analysisfunctional-coefficient modelsincidental parameterlatent groupslocal linear estimationpanel dataquantile regressionDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":"53 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135813183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bootstrap Inference in Cointegrating Regressions: Traditional and Self-Normalized Test Statistics 协整回归中的自举推理:传统和自归一化检验统计量

2区数学

Journal of Business & Economic Statistics Pub Date : 2023-10-18 DOI: 10.1080/07350015.2023.2271538

Karsten Reichold, Carsten Jentsch

{"title":"Bootstrap Inference in Cointegrating Regressions: Traditional and Self-Normalized Test Statistics","authors":"Karsten Reichold, Carsten Jentsch","doi":"10.1080/07350015.2023.2271538","DOIUrl":"https://doi.org/10.1080/07350015.2023.2271538","url":null,"abstract":"Traditional tests of hypotheses on the cointegrating vector are well known to suffer from severe size distortions in finite samples, especially when the data are characterized by large levels of endogeneity or error serial correlation. To address this issue, we combine a vector autoregressive (VAR) sieve bootstrap to construct critical values with a self-normalization approach that avoids direct estimation of long-run variance parameters when computing test statistics. To asymptotically justify this method, we prove bootstrap consistency for the self-normalized test statistics under mild conditions. In addition, the underlying bootstrap invariance principle allows us to prove bootstrap consistency also for traditional test statistics based on popular modified OLS estimators. Simulation results show that using bootstrap critical values instead of asymptotic critical values reduces size distortions associated with traditional test statistics considerably, but combining the VAR sieve bootstrap with self-normalization can lead to even less size distorted tests at the cost of only small power losses. We illustrate the usefulness of the VAR sieve bootstrap in empirical applications by analyzing the validity of the Fisher effect in 19 OECD countries.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135883511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Double machine learning for sample selection models+ 双机器学习的样本选择模型+

2区数学

Journal of Business & Economic Statistics Pub Date : 2023-10-16 DOI: 10.1080/07350015.2023.2271071

Michela Bia, Martin Huber, Lukáš Lafférs

{"title":"Double machine learning for sample selection models+","authors":"Michela Bia, Martin Huber, Lukáš Lafférs","doi":"10.1080/07350015.2023.2271071","DOIUrl":"https://doi.org/10.1080/07350015.2023.2271071","url":null,"abstract":"AbstractThis paper considers the evaluation of discretely distributed treatments when outcomes are only observed for a subpopulation due to sample selection or outcome attrition. For identification, we combine a selection-on-observables assumption for treatment assignment with either selection-on-observables or instrumental variable assumptions concerning the outcome attrition/sample selection process. We also consider dynamic confounding, meaning that covariates that jointly affect sample selection and the outcome may (at least partly) be influenced by the treatment. To control in a data-driven way for a potentially high dimensional set of pre- and/or post-treatment covariates, we adapt the double machine learning framework for treatment evaluation to sample selection problems. We make use of (a) Neyman-orthogonal, doubly robust, and efficient score functions, which imply the robustness of treatment effect estimation to moderate regularization biases in the machine learning- based estimation of the outcome, treatment, or sample selection models and (b) sample splitting (or cross-fitting) to prevent overfitting bias. We demonstrate that the proposed estimators are asymptotically normal and root-n consistent and investigate their finite sample properties in a simulation study. We also apply our proposed methodology to the Job Corps data. The estimator is available in the causalweight package for the statistical software R.Keywords: sample selectiondouble machine learningdoubly robust estimationefficient scoreDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136078023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Discussion of Levon Barseghyan and Francesca Molinari’s “Risk Preference Types, Limited Consideration, and Welfare” 对Barseghyan和Francesca Molinari“风险偏好类型、有限考虑和福利”的讨论

2区数学

Journal of Business & Economic Statistics Pub Date : 2023-10-02 DOI: 10.1080/07350015.2023.2223592

Julie Holland Mortimer

{"title":"Discussion of Levon Barseghyan and Francesca Molinari’s “Risk Preference Types, Limited Consideration, and Welfare”","authors":"Julie Holland Mortimer","doi":"10.1080/07350015.2023.2223592","DOIUrl":"https://doi.org/10.1080/07350015.2023.2223592","url":null,"abstract":"Click to increase image sizeClick to decrease image size Notes1 Figure 1 is available online at: https://insurance.ohio.gov/wps/wcm/connect/gov/ea3f5cf0-181b-47ed-bdbd-a060b6613a4d/CompleteAutoGuide+2022.pdf?MOD=AJPERES&CONVERT_TO=url&CACHEID=ROOTWORKSPACE.Z18_M1HGGIK0N0JO00QO9DDDDM3000-ea3f5cf0-181b-47ed-bdbd-a060b6613a4d-n.SMM7v. I don’t know the state from which the authors’ data are collected, and the format of insurance quotes in the author’s data may differ from that shown here; this is meant for illustrative purposes only.2 See https://www.bankrate.com/insurance/car/car-insurance-deductible//#types. Accessed on January 5, 2023; current version was updated March 2, 2023.3 See https://www.forbes.com/advisor/car-insurance/comprehensive-vs-collision-auto-insurance/. Last accessed on March 26, 2023.4 According to Statista, roughly 85% of new cars (and 40% of used cars) are financed. The number of new cars that are leased has been falling in recent years from a high of almost 1 in 3 in 2020 to roughly 1 in 5 today.5 See https://www.forbes.com/advisor/car-insurance/comprehensive-vs-collision-auto-insurance/. Last accessed on March 26, 2023.6 See https://www.bankrate.com/insurance/car/car-insurance-deductible//#types. Accessed on March 26, 2023.7 It’s important to recognize that we need ex-ante estimates of claim amounts, analogous to claim probabilities.8 See https://www.moneygeek.com/insurance/auto/do-i-need-comprehensive-collision/. Accessed on March 26, 2023.9 See https://www.bankrate.com/insurance/car/car-insurance-deductible//#impact. Accessed on March 26, 2023.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":"35 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135901051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Context-Dependent Heterogeneous Preferences: A Comment on Barseghyan and Molinari (2023) 语境依赖的异质性偏好:对Barseghyan和Molinari(2023)的评析

2区数学

Journal of Business & Economic Statistics Pub Date : 2023-10-02 DOI: 10.1080/07350015.2023.2216740

Matias D. Cattaneo, Xinwei Ma, Yusufcan Masatlioglu

引用次数: 0