Journal of Econometrics最新文献_第8页

Scenario-based quantile connectedness of the U.S. interbank liquidity risk network 美国银行间流动性风险网络的基于情景的量化关联性

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2024-09-01 DOI: 10.1016/j.jeconom.2024.105786

Tomohiro Ando , Jushan Bai , Lina Lu , Cindy M. Vojtech

{"title":"Scenario-based quantile connectedness of the U.S. interbank liquidity risk network","authors":"Tomohiro Ando , Jushan Bai , Lina Lu , Cindy M. Vojtech","doi":"10.1016/j.jeconom.2024.105786","DOIUrl":"10.1016/j.jeconom.2024.105786","url":null,"abstract":"<div><div><span><span>We characterize the U.S.<span> interbank liquidity risk network based on a supervisory dataset, using a scenario-based quantile network connectedness approach. In terms of methodology, we consider a quantile vector autoregressive model with unobserved heterogeneity and propose a </span></span>Bayesian nuclear norm estimation method. A common factor structure is employed to deal with unobserved heterogeneity that may exhibit endogeneity within the network. Then we develop a scenario-based quantile network connectedness framework by accommodating various economic scenarios, through a scenario-based moving average expression of the model where forecast error variance decomposition under a future pre-specified scenario is derived. The methodology is used to study the quantile-dependent liquidity risk network among large U.S. bank holding companies. The estimated quantile liquidity risk network connectedness measures could be useful for bank supervision and </span>financial stability<span><span> monitoring by providing leading indicators of the system-wide liquidity risk connectedness not only at the median but also at the </span>tails or even under a pre-specified scenario. The measures also help identify systemically important banks and vulnerable banks in the liquidity risk transmission of the U.S. banking system.</span></div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"244 2","pages":"Article 105786"},"PeriodicalIF":9.9,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142723206","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

Tuning-parameter-free propensity score matching approach for causal inference under shape restriction 形状限制条件下用于因果推断的无调整参数倾向得分匹配方法

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2024-08-01 DOI: 10.1016/j.jeconom.2024.105829

Yukun Liu , Jing Qin

引用次数: 0

Fixed-b asymptotics for panel models with two-way clustering 具有双向聚类的面板模型的固定B渐近线

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2024-08-01 DOI: 10.1016/j.jeconom.2024.105831

Kaicheng Chen, Timothy J. Vogelsang

{"title":"Fixed-b asymptotics for panel models with two-way clustering","authors":"Kaicheng Chen, Timothy J. Vogelsang","doi":"10.1016/j.jeconom.2024.105831","DOIUrl":"10.1016/j.jeconom.2024.105831","url":null,"abstract":"<div><p>This paper studies a cluster robust variance estimator proposed by Chiang, Hansen and Sasaki (2024) for linear panels. First, we show algebraically that this variance estimator (CHS estimator, hereafter) is a linear combination of three common variance estimators: the one-way unit cluster estimator, the “HAC of averages” estimator, and the “average of HACs” estimator. Based on this finding, we obtain a fixed-<span><math><mi>b</mi></math></span> asymptotic result for the CHS estimator and corresponding test statistics as the cross-section and time sample sizes jointly go to infinity. Furthermore, we propose two simple bias-corrected versions of the variance estimator and derive the fixed-<span><math><mi>b</mi></math></span> limits. In a simulation study, we find that the two bias-corrected variance estimators along with fixed-<span><math><mi>b</mi></math></span> critical values provide improvements in finite sample coverage probabilities. We illustrate the impact of bias-correction and use of the fixed-<span><math><mi>b</mi></math></span> critical values on inference in an empirical example on the relationship between industry profitability and market concentration.</p></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"244 1","pages":"Article 105831"},"PeriodicalIF":9.9,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142050189","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 for sparse idiosyncratic components in factor-augmented regression models 检验因子增强回归模型中的稀疏特异性成分

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2024-08-01 DOI: 10.1016/j.jeconom.2024.105845

Jad Beyhum , Jonas Striaukas

引用次数: 0

On uniform confidence intervals for the tail index and the extreme quantile 关于尾部指数和极值量级的统一置信区间

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2024-08-01 DOI: 10.1016/j.jeconom.2024.105865

Yuya Sasaki , Yulong Wang

引用次数: 0

High-dimensional model-assisted inference for treatment effects with multi-valued treatments 多值治疗效果的高维模型辅助推论

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2024-08-01 DOI: 10.1016/j.jeconom.2024.105852

Wenfu Xu , Zhiqiang Tan

{"title":"High-dimensional model-assisted inference for treatment effects with multi-valued treatments","authors":"Wenfu Xu , Zhiqiang Tan","doi":"10.1016/j.jeconom.2024.105852","DOIUrl":"10.1016/j.jeconom.2024.105852","url":null,"abstract":"<div><div>Consider estimation of average treatment effects with multi-valued treatments using augmented inverse probability weighted (IPW) estimators, depending on outcome regression and propensity score models in high-dimensional settings. These regression models are often fitted by regularized likelihood-based estimation, while ignoring how the fitted functions are used in the subsequent inference about the treatment parameters. Such separate estimation can be associated with known difficulties in existing methods. We develop regularized calibrated estimation for fitting propensity score and outcome regression models, where sparsity-including penalties are employed to facilitate variable selection but the loss functions are carefully chosen such that valid confidence intervals can be obtained under possible model misspecification. Unlike in the case of binary treatments, the usual augmented IPW estimator is generalized to ensure just-identification of parameters from new calibration equations. For propensity score estimation, the new loss function and estimating functions are directly tied to achieving covariate balance between weighted treatment groups. We develop practical algorithms for computing the regularized calibrated estimators with group Lasso by innovatively exploiting Fisher scoring, and provide rigorous high-dimensional analysis for the resulting augmented IPW estimators under suitable sparsity conditions, while tackling technical issues absent or overlooked in previous analyses. We present simulation studies and an empirical application to estimate the effects of maternal smoking on birth weights. The proposed methods are implemented in the R package <span>mRCAL</span>.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"244 1","pages":"Article 105852"},"PeriodicalIF":9.9,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0304407624001970/pdfft?md5=bcd51f3983e07a702d8ed2d7dc8fdb38&pid=1-s2.0-S0304407624001970-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142314950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Latent utility and permutation invariance: A revealed preference approach 潜在效用和排列不变性：揭示偏好法

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2024-08-01 DOI: 10.1016/j.jeconom.2024.105844

Roy Allen , John Rehbeck

引用次数: 0

Heterogeneous treatment effect bounds under sample selection with an application to the effects of social media on political polarization 样本选择下的异质性处理效应边界，应用于社交媒体对政治极化的影响

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2024-08-01 DOI: 10.1016/j.jeconom.2024.105856

Phillip Heiler

{"title":"Heterogeneous treatment effect bounds under sample selection with an application to the effects of social media on political polarization","authors":"Phillip Heiler","doi":"10.1016/j.jeconom.2024.105856","DOIUrl":"10.1016/j.jeconom.2024.105856","url":null,"abstract":"<div><p>We propose a method for estimation and inference for bounds for heterogeneous causal effect parameters in general sample selection models where the treatment can affect whether an outcome is observed and no exclusion restrictions are available. The method provides conditional effect bounds as functions of policy relevant pre-treatment variables. It allows for conducting valid statistical inference on the unidentified conditional effects. We use a flexible debiased/double machine learning approach that can accommodate non-linear functional forms and high-dimensional confounders. Easily verifiable high-level conditions for estimation, misspecification robust confidence intervals, and uniform confidence bands are provided as well. We re-analyze data from a large scale field experiment on Facebook on counter-attitudinal news subscription with attrition. Our method yields substantially tighter effect bounds compared to conventional methods and suggests depolarization effects for younger users.</p></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"244 1","pages":"Article 105856"},"PeriodicalIF":9.9,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S030440762400201X/pdfft?md5=6a6addc12c3ac7b4b64d5b0fb4fdde73&pid=1-s2.0-S030440762400201X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142239562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GMM estimation for high-dimensional panel data models 高维面板数据模型的 GMM 估算

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2024-08-01 DOI: 10.1016/j.jeconom.2024.105853

Tingting Cheng , Chaohua Dong , Jiti Gao , Oliver Linton

{"title":"GMM estimation for high-dimensional panel data models","authors":"Tingting Cheng , Chaohua Dong , Jiti Gao , Oliver Linton","doi":"10.1016/j.jeconom.2024.105853","DOIUrl":"10.1016/j.jeconom.2024.105853","url":null,"abstract":"<div><p>In this paper, we study a class of high dimensional moment restriction panel data models with interactive effects, where the factors are unobserved and these factor loadings are nonparametrically unknown smooth functions of individual characteristic variables. We allow the dimension of the parameter vector and the number of moment conditions to diverge with the sample size. This is a very general framework and is closely related to many existing linear and nonlinear panel data models. In order to estimate the unknown parameters, factors and factor loadings, we propose a sieve-based generalized method of moments estimation method and we show that under a set of simple identification conditions, all those unknown quantities can be consistently estimated. Further we establish asymptotic distributions of the proposed estimators. In addition, we propose tests for over-identification, specification of factor loading functions, and establish their large sample properties. Moreover, a number of simulation studies are conducted to examine the performance of the proposed estimators and test statistics in finite samples. An empirical example on stock return prediction is studied to demonstrate both the empirical relevance and the applicability of the proposed framework and corresponding estimation and testing methods.</p></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"244 1","pages":"Article 105853"},"PeriodicalIF":9.9,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0304407624001982/pdfft?md5=d3431c6c6b2ea9a2232bb95323a846ed&pid=1-s2.0-S0304407624001982-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142239564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Measuring diagnostic test performance using imperfect reference tests: A partial identification approach 利用不完善的参考测试衡量诊断测试的性能：部分鉴定方法

IF 9.9 3区经济学

Journal of Econometrics Pub Date : 2024-08-01 DOI: 10.1016/j.jeconom.2024.105842

Filip Obradović

{"title":"Measuring diagnostic test performance using imperfect reference tests: A partial identification approach","authors":"Filip Obradović","doi":"10.1016/j.jeconom.2024.105842","DOIUrl":"10.1016/j.jeconom.2024.105842","url":null,"abstract":"<div><p>Diagnostic tests are almost never perfect. Studies quantifying their performance use knowledge of the true health status, measured with a reference diagnostic test. Researchers commonly assume that the reference test is perfect, which is often not the case in practice. When the assumption fails, conventional studies identify “apparent” performance or performance with respect to the reference, but not true performance. This paper provides the smallest possible bounds on the measures of true performance — sensitivity (true positive rate) and specificity (true negative rate), or equivalently false positive and negative rates, in standard settings. Implied bounds on policy-relevant parameters are derived: (1) Prevalence in screened populations; (2) Predictive values. Methods for inference based on moment inequalities are used to construct uniformly consistent confidence sets in level over a relevant family of data distributions. Emergency Use Authorization (EUA) and independent study data for the BinaxNOW COVID-19 antigen test demonstrate that the bounds can be very informative. Analysis reveals that the estimated false negative rates for symptomatic and asymptomatic patients are up to 3.17 and 4.59 times higher than the frequently cited “apparent” false negative rate. Further applicability of the results in the context of imperfect proxies such as survey responses and imputed protected classes is indicated.</p></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"244 1","pages":"Article 105842"},"PeriodicalIF":9.9,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142168782","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