Journal of Econometrics最新文献

{"title":"Extreme expectile estimation for short-tailed data","authors":"Abdelaati Daouia ,&nbsp;Simone A. Padoan ,&nbsp;Gilles Stupfler","doi":"10.1016/j.jeconom.2024.105770","DOIUrl":"https://doi.org/10.1016/j.jeconom.2024.105770","url":null,"abstract":"<div><p>The use of expectiles in risk management has recently gathered remarkable momentum due to their excellent axiomatic and probabilistic properties. In particular, the class of elicitable law-invariant coherent risk measures only consists of expectiles. While the theory of expectile estimation at central levels is substantial, tail estimation at extreme levels has so far only been considered when the tail of the underlying distribution is heavy. This article is the first work to handle the short-tailed setting where the loss (<em>e.g.</em> negative log-returns) distribution of interest is bounded to the right and the corresponding extreme value index is negative. This is motivated by the assessment of long-term market risk carried by low-frequency (<em>e.g.</em> weekly) returns of equities that show evidence of being generated from short-tailed distributions. We derive an asymptotic expansion of tail expectiles in this challenging context under a general second-order extreme value condition, which allows to come up with two semiparametric estimators of extreme expectiles, and with their asymptotic properties in a general model of strictly stationary but weakly dependent observations. We also extend the applicability of the proposed method to the regression setting. A simulation study and a real data analysis from a forecasting perspective are performed to compare the proposed competing estimation procedures.</p></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"241 2","pages":"Article 105770"},"PeriodicalIF":6.3,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141072886","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}

{"title":"Measuring tail risk","authors":"Maik Dierkes ,&nbsp;Fabian Hollstein ,&nbsp;Marcel Prokopczuk ,&nbsp;Christoph Matthias Würsig","doi":"10.1016/j.jeconom.2024.105769","DOIUrl":"https://doi.org/10.1016/j.jeconom.2024.105769","url":null,"abstract":"<div><p>We comprehensively investigate the usefulness of tail risk measures proposed in the literature. We evaluate their statistical as well as their economic validity. The option-implied measure of Bollerslev and Todorov (2011b) (<span><math><mrow><mi>B</mi><mi>T</mi><mn>11</mn><mi>Q</mi></mrow></math></span>) performs best overall. While some other tail risk measures excel at specialized tasks, <span><math><mrow><mi>B</mi><mi>T</mi><mn>11</mn><mi>Q</mi></mrow></math></span> performs well in all tests: First, <span><math><mrow><mi>B</mi><mi>T</mi><mn>11</mn><mi>Q</mi></mrow></math></span> can predict both future tail events and future tail volatility. Second, it has predictive power for returns in both the time series and the cross-section, as well as for real economic activity. Finally, a simulation analysis shows that the main driver of performance is measurement error.</p></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"241 2","pages":"Article 105769"},"PeriodicalIF":6.3,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0304407624001155/pdfft?md5=e585dfb93408d24930d48e978ccaf9c5&pid=1-s2.0-S0304407624001155-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141084604","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}

{"title":"Correcting attrition bias using changes-in-changes","authors":"Dalia Ghanem ,&nbsp;Sarojini Hirshleifer ,&nbsp;Désiré Kédagni ,&nbsp;Karen Ortiz-Becerra","doi":"10.1016/j.jeconom.2024.105737","DOIUrl":"https://doi.org/10.1016/j.jeconom.2024.105737","url":null,"abstract":"<div><p>Attrition is a common and potentially important threat to internal validity in treatment effect studies. We extend the changes-in-changes approach to identify the average treatment effect for respondents and the entire study population in the presence of attrition. Our method, which exploits baseline outcome data, can be applied to randomized experiments as well as quasi-experimental difference-in-difference designs. A formal comparison highlights that while widely used corrections typically impose restrictions on whether or how response depends on treatment, our proposed attrition correction exploits restrictions on the outcome model. We further show that the conditions required for our correction can accommodate a broad class of response models that depend on treatment in an arbitrary way. We illustrate the implementation of the proposed corrections in an application to a large-scale randomized experiment.</p></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"241 2","pages":"Article 105737"},"PeriodicalIF":6.3,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140818395","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}

{"title":"Wild bootstrap inference for instrumental variables regressions with weak and few clusters","authors":"Wenjie Wang ,&nbsp;Yichong Zhang","doi":"10.1016/j.jeconom.2024.105727","DOIUrl":"https://doi.org/10.1016/j.jeconom.2024.105727","url":null,"abstract":"<div><p>We study the wild bootstrap inference for instrumental variable regressions under an alternative asymptotic framework that the number of independent clusters is fixed, the size of each cluster diverges to infinity, and the within cluster dependence is sufficiently weak. We first show that the wild bootstrap Wald test controls size asymptotically up to a small error as long as the parameters of endogenous variables are strongly identified in at least one of the clusters. Second, we establish the conditions for the bootstrap tests to have power against local alternatives. We further develop a wild bootstrap Anderson–Rubin test for the full-vector inference and show that it controls size asymptotically even under weak identification in all clusters. We illustrate their good performance using simulations and provide an empirical application to a well-known dataset about US local labor markets.</p></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"241 1","pages":"Article 105727"},"PeriodicalIF":6.3,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140321564","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}

{"title":"Spectral clustering with variance information for group structure estimation in panel data","authors":"Lu Yu ,&nbsp;Jiaying Gu ,&nbsp;Stanislav Volgushev","doi":"10.1016/j.jeconom.2024.105709","DOIUrl":"https://doi.org/10.1016/j.jeconom.2024.105709","url":null,"abstract":"<div><p>Consider a panel data setting where repeated observations on individuals are available. Often it is reasonable to assume that there exist groups of individuals that share similar effects of observed characteristics, but the grouping is typically unknown in advance. We first conduct a local analysis which reveals that the variances of the individual coefficient estimates contain useful information for the estimation of group structure. We then propose a method to estimate unobserved groupings for general panel data models that explicitly accounts for the variance information. Our proposed method remains computationally feasible with a large number of individuals and/or repeated measurements on each individual. The developed ideas can also be applied even when individual-level data are not available and only parameter estimates together with some quantification of estimation uncertainty are given to the researcher. A thorough simulation study demonstrates superior performance of our method than existing methods and we apply the method to two empirical applications.</p></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"241 1","pages":"Article 105709"},"PeriodicalIF":6.3,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140138578","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}

{"title":"Score-type tests for normal mixtures","authors":"Dante Amengual, Xinyue Bei, Marine Carrasco, Enrique Sentana","doi":"10.1016/j.jeconom.2024.105717","DOIUrl":"https://doi.org/10.1016/j.jeconom.2024.105717","url":null,"abstract":"Testing normality against discrete normal mixtures is complex because some parameters turn increasingly underidentified along alternative ways of approaching the null, others are inequality constrained, and several higher-order derivatives become identically 0. These problems make the maximum of the alternative model log-likelihood function numerically unreliable. We propose score-type tests asymptotically equivalent to the likelihood ratio as the largest of two simple intuitive statistics that only require estimation under the null. One novelty of our approach is that we treat symmetrically both ways of writing the null hypothesis without excluding any region of the parameter space. We derive the asymptotic distribution of our tests under the null and sequences of local alternatives. We also show that their asymptotic distribution is the same whether applied to observations or standardized residuals from heteroskedastic regression models. Finally, we study their power in simulations and apply them to the residuals of Mincer earnings functions.","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"148 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140154183","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}

{"title":"Efficiency bounds for moment condition models with mixed identification strength","authors":"Prosper Dovonon, Yves F. Atchadé, Firmin Doko Tchatoka","doi":"10.1016/j.jeconom.2024.105723","DOIUrl":"https://doi.org/10.1016/j.jeconom.2024.105723","url":null,"abstract":"Moment condition models with mixed identification strength are models that are point identified but with estimating moment functions that are allowed to drift to 0 uniformly over the parameter space. Even though identification fails in the limit, depending on how slow the moment functions vanish, consistent estimation is possible. Existing estimators such as the generalized method of moment (GMM) estimator exhibit a pattern of nonstandard or even heterogeneous rate of convergence that materializes by some parameter directions being estimated at a slower rate than others. This paper derives asymptotic semiparametric efficiency bounds for regular estimators of parameters of these models. We show that GMM estimators are regular and that the so-called two-step GMM estimator – using the inverse of estimating function’s variance as weighting matrix – is semiparametrically efficient as it reaches the minimum variance attainable by regular estimators. This estimator is also asymptotically minimax efficient with respect to a large family of loss functions. Monte Carlo simulations are provided that confirm these results.","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"59 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140154621","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}

{"title":"Predictive ability tests with possibly overlapping models","authors":"Valentina Corradi ,&nbsp;Jack Fosten ,&nbsp;Daniel Gutknecht","doi":"10.1016/j.jeconom.2024.105716","DOIUrl":"https://doi.org/10.1016/j.jeconom.2024.105716","url":null,"abstract":"<div><p>This paper provides novel tests for comparing out-of-sample predictive ability of two or more competing models that are possibly overlapping. The tests do not require pre-testing, they allow for dynamic misspecification and are valid under different estimation schemes and loss functions. In pairwise model comparisons, the test is constructed by adding a random perturbation to both the numerator and denominator of a standard Diebold–Mariano test statistic. This prevents degeneracy in the presence of overlapping models but becomes asymptotically negligible otherwise. The test is shown to control the Type I error probability asymptotically at the nominal level, uniformly over all null data generating processes. A similar idea is used to develop a superior predictive ability test for the comparison of multiple models against a benchmark. Monte Carlo simulations demonstrate that our tests exhibit very good size control in finite samples reducing both over- and under-rejection relative to its competitors. Finally, an application to forecasting U.S. excess bond returns provides evidence in favour of models using macroeconomic factors.</p></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"241 1","pages":"Article 105716"},"PeriodicalIF":6.3,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140096299","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}

{"title":"No star is good news: A unified look at rerandomization based on p-values from covariate balance tests","authors":"Anqi Zhao ,&nbsp;Peng Ding","doi":"10.1016/j.jeconom.2024.105724","DOIUrl":"https://doi.org/10.1016/j.jeconom.2024.105724","url":null,"abstract":"<div><p>Randomized experiments balance all covariates on average and are considered the gold standard for estimating treatment effects. Chance imbalances are nonetheless common in realized treatment allocations. To inform readers of the comparability of treatment groups at baseline, contemporary scientific publications often report covariate balance tables with not only covariate means by treatment group but also the associated <span><math><mi>p</mi></math></span>-values from significance tests of their differences. The practical need to avoid small <span><math><mi>p</mi></math></span>-values as indicators of poor balance motivates balance check and rerandomization based on these <span><math><mi>p</mi></math></span>-values from covariate balance tests (ReP) as an attractive tool for improving covariate balance in designing randomized experiments. Despite the intuitiveness of such strategy and its possibly already widespread use in practice, the literature lacks results about its implications on subsequent inference, subjecting many effectively rerandomized experiments to possibly inefficient analyses. To fill this gap, we examine a variety of potentially useful schemes for ReP and quantify their impact on subsequent inference. Specifically, we focus on three estimators of the average treatment effect from the unadjusted, additive, and interacted linear regressions of the outcome on treatment, respectively, and derive their asymptotic sampling properties under ReP. The main findings are threefold. First, the estimator from the interacted regression is asymptotically the most efficient under all ReP schemes examined, and permits convenient regression-assisted inference identical to that under complete randomization. Second, ReP, in contrast to complete randomization, improves the asymptotic efficiency of the estimators from the unadjusted and additive regressions. Standard regression analyses are accordingly still valid but in general overconservative. Third, ReP reduces the asymptotic conditional biases of the three estimators and improves their coherence in terms of mean squared difference. These results establish ReP as a convenient tool for improving covariate balance in designing randomized experiments, and we recommend using the interacted regression for analyzing data from ReP designs.</p></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"241 1","pages":"Article 105724"},"PeriodicalIF":6.3,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140096300","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}

{"title":"Bayesian estimation of cluster covariance matrices of unknown form","authors":"Drew Creal ,&nbsp;Jaeho Kim","doi":"10.1016/j.jeconom.2024.105725","DOIUrl":"https://doi.org/10.1016/j.jeconom.2024.105725","url":null,"abstract":"<div><p>We develop a flexible Bayesian model for cluster covariance matrices in large dimensions where the number of clusters and the assignment of cross-sectional units to a cluster are a-priori unknown and estimated from the data. In a cluster covariance matrix, the variances and covariances are equal within each diagonal block, while the covariances are equal in each off-diagonal block. This reduces the number of parameters by pooling those parameters together that are in the same cluster. In order to treat the number of clusters and the cluster assignments as unknowns, we build a random partition model which assigns a prior distribution over the space of partitions of the data into clusters. Sampling from the posterior over the space of partitions creates a flexible estimator because it averages across a wide set of cluster covariance matrices. We illustrate our methods on linear factor models and large vector autoregressions.</p></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"241 1","pages":"Article 105725"},"PeriodicalIF":6.3,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140052404","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}