arXiv - ECON - Econometrics最新文献_第2页

Bayesian Dynamic Factor Models for High-dimensional Matrix-valued Time Series 高维矩阵值时间序列的贝叶斯动态因子模型

arXiv - ECON - Econometrics Pub Date : 2024-09-12 DOI: arxiv-2409.08354

Wei Zhang

引用次数: 0

Bootstrap Adaptive Lasso Solution Path Unit Root Tests 引导自适应套索求解路径单位根检验

arXiv - ECON - Econometrics Pub Date : 2024-09-12 DOI: arxiv-2409.07859

Martin C. Arnold, Thilo Reinschlüssel

引用次数: 0

Substitution in the perturbed utility route choice model 扰动效用路线选择模型中的替代问题

arXiv - ECON - Econometrics Pub Date : 2024-09-12 DOI: arxiv-2409.08347

Mogens Fosgerau, Nikolaj Nielsen, Mads Paulsen, Thomas Kjær Rasmussen, Rui Yao

引用次数: 0

Testing for a Forecast Accuracy Breakdown under Long Memory 测试长时记忆下的预测准确性分解

arXiv - ECON - Econometrics Pub Date : 2024-09-11 DOI: arxiv-2409.07087

Jannik Kreye, Philipp Sibbertsen

引用次数: 0

Estimation and Inference for Causal Functions with Multiway Clustered Data 多向聚类数据的因果函数估计与推理

arXiv - ECON - Econometrics Pub Date : 2024-09-10 DOI: arxiv-2409.06654

Nan Liu, Yanbo Liu, Yuya Sasaki

{"title":"Estimation and Inference for Causal Functions with Multiway Clustered Data","authors":"Nan Liu, Yanbo Liu, Yuya Sasaki","doi":"arxiv-2409.06654","DOIUrl":"https://doi.org/arxiv-2409.06654","url":null,"abstract":"This paper proposes methods of estimation and uniform inference for a general\u0000class of causal functions, such as the conditional average treatment effects\u0000and the continuous treatment effects, under multiway clustering. The causal\u0000function is identified as a conditional expectation of an adjusted\u0000(Neyman-orthogonal) signal that depends on high-dimensional nuisance\u0000parameters. We propose a two-step procedure where the first step uses machine\u0000learning to estimate the high-dimensional nuisance parameters. The second step\u0000projects the estimated Neyman-orthogonal signal onto a dictionary of basis\u0000functions whose dimension grows with the sample size. For this two-step\u0000procedure, we propose both the full-sample and the multiway cross-fitting\u0000estimation approaches. A functional limit theory is derived for these\u0000estimators. To construct the uniform confidence bands, we develop a novel\u0000resampling procedure, called the multiway cluster-robust sieve score bootstrap,\u0000that extends the sieve score bootstrap (Chen and Christensen, 2018) to the\u0000novel setting with multiway clustering. Extensive numerical simulations\u0000showcase that our methods achieve desirable finite-sample behaviors. We apply\u0000the proposed methods to analyze the causal relationship between mistrust levels\u0000in Africa and the historical slave trade. Our analysis rejects the null\u0000hypothesis of uniformly zero effects and reveals heterogeneous treatment\u0000effects, with significant impacts at higher levels of trade volumes.","PeriodicalId":501293,"journal":{"name":"arXiv - ECON - Econometrics","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184090","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

Enhancing Preference-based Linear Bandits via Human Response Time 通过人类响应时间增强基于偏好的线性匪帮

arXiv - ECON - Econometrics Pub Date : 2024-09-09 DOI: arxiv-2409.05798

Shen Li, Yuyang Zhang, Zhaolin Ren, Claire Liang, Na Li, Julie A. Shah

{"title":"Enhancing Preference-based Linear Bandits via Human Response Time","authors":"Shen Li, Yuyang Zhang, Zhaolin Ren, Claire Liang, Na Li, Julie A. Shah","doi":"arxiv-2409.05798","DOIUrl":"https://doi.org/arxiv-2409.05798","url":null,"abstract":"Binary human choice feedback is widely used in interactive preference\u0000learning for its simplicity, but it provides limited information about\u0000preference strength. To overcome this limitation, we leverage human response\u0000times, which inversely correlate with preference strength, as complementary\u0000information. Our work integrates the EZ-diffusion model, which jointly models\u0000human choices and response times, into preference-based linear bandits. We\u0000introduce a computationally efficient utility estimator that reformulates the\u0000utility estimation problem using both choices and response times as a linear\u0000regression problem. Theoretical and empirical comparisons with traditional\u0000choice-only estimators reveal that for queries with strong preferences (\"easy\"\u0000queries), choices alone provide limited information, while response times offer\u0000valuable complementary information about preference strength. As a result,\u0000incorporating response times makes easy queries more useful. We demonstrate\u0000this advantage in the fixed-budget best-arm identification problem, with\u0000simulations based on three real-world datasets, consistently showing\u0000accelerated learning when response times are incorporated.","PeriodicalId":501293,"journal":{"name":"arXiv - ECON - Econometrics","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184096","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

Uniform Estimation and Inference for Nonparametric Partitioning-Based M-Estimators 基于非参数划分的 M 估计器的统一估计与推理

arXiv - ECON - Econometrics Pub Date : 2024-09-09 DOI: arxiv-2409.05715

Matias D. Cattaneo, Yingjie Feng, Boris Shigida

{"title":"Uniform Estimation and Inference for Nonparametric Partitioning-Based M-Estimators","authors":"Matias D. Cattaneo, Yingjie Feng, Boris Shigida","doi":"arxiv-2409.05715","DOIUrl":"https://doi.org/arxiv-2409.05715","url":null,"abstract":"This paper presents uniform estimation and inference theory for a large class\u0000of nonparametric partitioning-based M-estimators. The main theoretical results\u0000include: (i) uniform consistency for convex and non-convex objective functions;\u0000(ii) optimal uniform Bahadur representations; (iii) optimal uniform (and mean\u0000square) convergence rates; (iv) valid strong approximations and feasible\u0000uniform inference methods; and (v) extensions to functional transformations of\u0000underlying estimators. Uniformity is established over both the evaluation point\u0000of the nonparametric functional parameter and a Euclidean parameter indexing\u0000the class of loss functions. The results also account explicitly for the\u0000smoothness degree of the loss function (if any), and allow for a possibly\u0000non-identity (inverse) link function. We illustrate the main theoretical and\u0000methodological results with four substantive applications: quantile regression,\u0000distribution regression, $L_p$ regression, and Logistic regression; many other\u0000possibly non-smooth, nonlinear, generalized, robust M-estimation settings are\u0000covered by our theoretical results. We provide detailed comparisons with the\u0000existing literature and demonstrate substantive improvements: we achieve the\u0000best (in some cases optimal) known results under improved (in some cases\u0000minimal) requirements in terms of regularity conditions and side rate\u0000restrictions. The supplemental appendix reports other technical results that\u0000may be of independent interest.","PeriodicalId":501293,"journal":{"name":"arXiv - ECON - Econometrics","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184097","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

The Surprising Robustness of Partial Least Squares 偏最小二乘法的惊人稳健性

arXiv - ECON - Econometrics Pub Date : 2024-09-09 DOI: arxiv-2409.05713

João B. Assunção, Pedro Afonso Fernandes

引用次数: 0

Bellwether Trades: Characteristics of Trades influential in Predicting Future Price Movements in Markets 风向标交易：对预测市场未来价格走势有影响的交易特征

arXiv - ECON - Econometrics Pub Date : 2024-09-08 DOI: arxiv-2409.05192

Tejas Ramdas, Martin T. Wells

引用次数: 0

Difference-in-Differences with Multiple Events 多事件差分法

arXiv - ECON - Econometrics Pub Date : 2024-09-08 DOI: arxiv-2409.05184

Lin-Tung Tsai

引用次数: 0