arXiv - ECON - Econometrics最新文献_第10页

On the power properties of inference for parameters with interval identified sets 论区间确定集参数推理的功率特性

arXiv - ECON - Econometrics Pub Date : 2024-07-29 DOI: arxiv-2407.20386

Federico A. Bugni, Mengsi Gao, Filip Obradovic, Amilcar Velez

引用次数: 0

Testing for the Asymmetric Optimal Hedge Ratios: With an Application to Bitcoin 测试非对称最佳对冲比率：比特币应用

arXiv - ECON - Econometrics Pub Date : 2024-07-29 DOI: arxiv-2407.19932

Abdulnasser Hatemi-J

{"title":"Testing for the Asymmetric Optimal Hedge Ratios: With an Application to Bitcoin","authors":"Abdulnasser Hatemi-J","doi":"arxiv-2407.19932","DOIUrl":"https://doi.org/arxiv-2407.19932","url":null,"abstract":"Reducing financial risk is of paramount importance to investors, financial\u0000institutions, and corporations. Since the pioneering contribution of Johnson\u0000(1960), the optimal hedge ratio based on futures is regularly utilized. The\u0000current paper suggests an explicit and efficient method for testing the null\u0000hypothesis of a symmetric optimal hedge ratio against an asymmetric alternative\u0000one within a multivariate setting. If the null is rejected, the position\u0000dependent optimal hedge ratios can be estimated via the suggested model. This\u0000approach is expected to enhance the accuracy of the implemented hedging\u0000strategies compared to the standard methods since it accounts for the fact that\u0000the source of risk depends on whether the investor is a buyer or a seller of\u0000the risky asset. An application is provided using spot and futures prices of\u0000Bitcoin. The results strongly support the view that the optimal hedge ratio for\u0000this cryptocurrency is position dependent. The investor that is long in Bitcoin\u0000has a much higher conditional optimal hedge ratio compared to the one that is\u0000short in the asset. The difference between the two conditional optimal hedge\u0000ratios is statistically significant, which has important repercussions for\u0000implementing risk management strategies.","PeriodicalId":501293,"journal":{"name":"arXiv - ECON - Econometrics","volume":"200 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141865025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Heterogeneous Grouping Structures in Panel Data 小组数据中的异质分组结构

arXiv - ECON - Econometrics Pub Date : 2024-07-28 DOI: arxiv-2407.19509

Katerina Chrysikou, George Kapetanios

{"title":"Heterogeneous Grouping Structures in Panel Data","authors":"Katerina Chrysikou, George Kapetanios","doi":"arxiv-2407.19509","DOIUrl":"https://doi.org/arxiv-2407.19509","url":null,"abstract":"In this paper we examine the existence of heterogeneity within a group, in\u0000panels with latent grouping structure. The assumption of within group\u0000homogeneity is prevalent in this literature, implying that the formation of\u0000groups alleviates cross-sectional heterogeneity, regardless of the prior\u0000knowledge of groups. While the latter hypothesis makes inference powerful, it\u0000can be often restrictive. We allow for models with richer heterogeneity that\u0000can be found both in the cross-section and within a group, without imposing the\u0000simple assumption that all groups must be heterogeneous. We further contribute\u0000to the method proposed by cite{su2016identifying}, by showing that the model\u0000parameters can be consistently estimated and the groups, while unknown, can be\u0000identifiable in the presence of different types of heterogeneity. Within the\u0000same framework we consider the validity of assuming both cross-sectional and\u0000within group homogeneity, using testing procedures. Simulations demonstrate\u0000good finite-sample performance of the approach in both classification and\u0000estimation, while empirical applications across several datasets provide\u0000evidence of multiple clusters, as well as reject the hypothesis of within group\u0000homogeneity.","PeriodicalId":501293,"journal":{"name":"arXiv - ECON - Econometrics","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141865022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Accounting for Nonresponse in Election Polls: Total Margin of Error 选举民意调查中的无回复计算：误差总幅度

arXiv - ECON - Econometrics Pub Date : 2024-07-27 DOI: arxiv-2407.19339

Jeff Dominitz, Charles F. Manski

引用次数: 0

Enhanced power enhancements for testing many moment equalities: Beyond the $2$- and $infty$-norm 测试多矩相等的增强功能：超越$2-和$infty-norm

arXiv - ECON - Econometrics Pub Date : 2024-07-25 DOI: arxiv-2407.17888

Anders Bredahl Kock, David Preinerstorfer

{"title":"Enhanced power enhancements for testing many moment equalities: Beyond the $2$- and $infty$-norm","authors":"Anders Bredahl Kock, David Preinerstorfer","doi":"arxiv-2407.17888","DOIUrl":"https://doi.org/arxiv-2407.17888","url":null,"abstract":"Tests based on the $2$- and $infty$-norm have received considerable\u0000attention in high-dimensional testing problems, as they are powerful against\u0000dense and sparse alternatives, respectively. The power enhancement principle of\u0000Fan et al. (2015) combines these two norms to construct tests that are powerful\u0000against both types of alternatives. Nevertheless, the $2$- and $infty$-norm\u0000are just two out of the whole spectrum of $p$-norms that one can base a test\u0000on. In the context of testing whether a candidate parameter satisfies a large\u0000number of moment equalities, we construct a test that harnesses the strength of\u0000all $p$-norms with $pin[2, infty]$. As a result, this test consistent against\u0000strictly more alternatives than any test based on a single $p$-norm. In\u0000particular, our test is consistent against more alternatives than tests based\u0000on the $2$- and $infty$-norm, which is what most implementations of the power\u0000enhancement principle target. We illustrate the scope of our general results by using them to construct a\u0000test that simultaneously dominates the Anderson-Rubin test (based on $p=2$) and\u0000tests based on the $infty$-norm in terms of consistency in the linear\u0000instrumental variable model with many (weak) instruments.","PeriodicalId":501293,"journal":{"name":"arXiv - ECON - Econometrics","volume":"127 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141771968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Starting Small: Prioritizing Safety over Efficacy in Randomized Experiments Using the Exact Finite Sample Likelihood 从小处着手：利用精确有限样本可能性在随机实验中优先考虑安全性而非有效性

arXiv - ECON - Econometrics Pub Date : 2024-07-25 DOI: arxiv-2407.18206

Neil Christy, A. E. Kowalski

引用次数: 0

Causal modelling without counterfactuals and individualised effects 没有反事实和个性化效应的因果建模

arXiv - ECON - Econometrics Pub Date : 2024-07-24 DOI: arxiv-2407.17385

Benedikt Höltgen, Robert C. Williamson

引用次数: 0

Identification and inference of outcome conditioned partial effects of general interventions 识别和推断以结果为条件的一般干预措施的部分效果

arXiv - ECON - Econometrics Pub Date : 2024-07-24 DOI: arxiv-2407.16950

Zhengyu Zhang, Zequn Jin, Lihua Lin

{"title":"Identification and inference of outcome conditioned partial effects of general interventions","authors":"Zhengyu Zhang, Zequn Jin, Lihua Lin","doi":"arxiv-2407.16950","DOIUrl":"https://doi.org/arxiv-2407.16950","url":null,"abstract":"This paper proposes a new class of distributional causal quantities, referred\u0000to as the textit{outcome conditioned partial policy effects} (OCPPEs), to\u0000measure the textit{average} effect of a general counterfactual intervention of\u0000a target covariate on the individuals in different quantile ranges of the\u0000outcome distribution. The OCPPE approach is valuable in several aspects: (i) Unlike the\u0000unconditional quantile partial effect (UQPE) that is not $sqrt{n}$-estimable,\u0000an OCPPE is $sqrt{n}$-estimable. Analysts can use it to capture heterogeneity\u0000across the unconditional distribution of $Y$ as well as obtain accurate\u0000estimation of the aggregated effect at the upper and lower tails of $Y$. (ii)\u0000The semiparametric efficiency bound for an OCPPE is explicitly derived. (iii)\u0000We propose an efficient debiased estimator for OCPPE, and provide feasible\u0000uniform inference procedures for the OCPPE process. (iv) The efficient doubly\u0000robust score for an OCPPE can be used to optimize infinitesimal nudges to a\u0000continuous treatment by maximizing a quantile specific Empirical Welfare\u0000function. We illustrate the method by analyzing how anti-smoking policies\u0000impact low percentiles of live infants' birthweights.","PeriodicalId":501293,"journal":{"name":"arXiv - ECON - Econometrics","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141771971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bayesian modelling of VAR precision matrices using stochastic block networks 利用随机块网络对 VAR 精确矩阵进行贝叶斯建模

arXiv - ECON - Econometrics Pub Date : 2024-07-23 DOI: arxiv-2407.16349

Florian Huber, Gary Koop, Massimiliano Marcellino, Tobias Scheckel

引用次数: 0

Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction 估算随机实验中的分布式治疗效果：减少方差的机器学习

arXiv - ECON - Econometrics Pub Date : 2024-07-22 DOI: arxiv-2407.16037

Undral Byambadalai, Tatsushi Oka, Shota Yasui

{"title":"Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction","authors":"Undral Byambadalai, Tatsushi Oka, Shota Yasui","doi":"arxiv-2407.16037","DOIUrl":"https://doi.org/arxiv-2407.16037","url":null,"abstract":"We propose a novel regression adjustment method designed for estimating\u0000distributional treatment effect parameters in randomized experiments.\u0000Randomized experiments have been extensively used to estimate treatment effects\u0000in various scientific fields. However, to gain deeper insights, it is essential\u0000to estimate distributional treatment effects rather than relying solely on\u0000average effects. Our approach incorporates pre-treatment covariates into a\u0000distributional regression framework, utilizing machine learning techniques to\u0000improve the precision of distributional treatment effect estimators. The\u0000proposed approach can be readily implemented with off-the-shelf machine\u0000learning methods and remains valid as long as the nuisance components are\u0000reasonably well estimated. Also, we establish the asymptotic properties of the\u0000proposed estimator and present a uniformly valid inference method. Through\u0000simulation results and real data analysis, we demonstrate the effectiveness of\u0000integrating machine learning techniques in reducing the variance of\u0000distributional treatment effect estimators in finite samples.","PeriodicalId":501293,"journal":{"name":"arXiv - ECON - Econometrics","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141771970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0