arXiv - ECON - Econometrics最新文献_第5页

BayesSRW: Bayesian Sampling and Re-weighting approach for variance reduction BayesSRW：减少方差的贝叶斯取样和再加权方法

arXiv - ECON - Econometrics Pub Date : 2024-08-28 DOI: arxiv-2408.15454

Carol Liu

{"title":"BayesSRW: Bayesian Sampling and Re-weighting approach for variance reduction","authors":"Carol Liu","doi":"arxiv-2408.15454","DOIUrl":"https://doi.org/arxiv-2408.15454","url":null,"abstract":"In this paper, we address the challenge of sampling in scenarios where\u0000limited resources prevent exhaustive measurement across all subjects. We\u0000consider a setting where samples are drawn from multiple groups, each following\u0000a distribution with unknown mean and variance parameters. We introduce a novel\u0000sampling strategy, motivated simply by Cauchy-Schwarz inequality, which\u0000minimizes the variance of the population mean estimator by allocating samples\u0000proportionally to both the group size and the standard deviation. This approach\u0000improves the efficiency of sampling by focusing resources on groups with\u0000greater variability, thereby enhancing the precision of the overall estimate.\u0000Additionally, we extend our method to a two-stage sampling procedure in a Bayes\u0000approach, named BayesSRW, where a preliminary stage is used to estimate the\u0000variance, which then informs the optimal allocation of the remaining sampling\u0000budget. Through simulation examples, we demonstrate the effectiveness of our\u0000approach in reducing estimation uncertainty and providing more reliable\u0000insights in applications ranging from user experience surveys to\u0000high-dimensional peptide array studies.","PeriodicalId":501293,"journal":{"name":"arXiv - ECON - Econometrics","volume":"67 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184150","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 effects of data preprocessing on probability of default model fairness 数据预处理对违约概率模型公平性的影响

arXiv - ECON - Econometrics Pub Date : 2024-08-28 DOI: arxiv-2408.15452

Di Wu

引用次数: 0

Endogenous Treatment Models with Social Interactions: An Application to the Impact of Exercise on Self-Esteem 具有社会互动的内生治疗模型：应用于运动对自尊的影响

arXiv - ECON - Econometrics Pub Date : 2024-08-26 DOI: arxiv-2408.13971

Zhongjian Lin, Francis Vella

引用次数: 0

Modeling the Dynamics of Growth in Master-Planned Communities 总体规划社区的增长动态建模

arXiv - ECON - Econometrics Pub Date : 2024-08-26 DOI: arxiv-2408.14214

Christopher K. Allsup, Irene S. Gabashvili

引用次数: 0

Double/Debiased CoCoLASSO of Treatment Effects with Mismeasured High-Dimensional Control Variables 误测高维控制变量治疗效果的双重/偏差 CoCoLASSO

arXiv - ECON - Econometrics Pub Date : 2024-08-26 DOI: arxiv-2408.14671

Geonwoo Kim, Suyong Song

{"title":"Double/Debiased CoCoLASSO of Treatment Effects with Mismeasured High-Dimensional Control Variables","authors":"Geonwoo Kim, Suyong Song","doi":"arxiv-2408.14671","DOIUrl":"https://doi.org/arxiv-2408.14671","url":null,"abstract":"We develop an estimator for treatment effects in high-dimensional settings\u0000with additive measurement error, a prevalent challenge in modern econometrics.\u0000We introduce the Double/Debiased Convex Conditioned LASSO (Double/Debiased\u0000CoCoLASSO), which extends the double/debiased machine learning framework to\u0000accommodate mismeasured covariates. Our principal contributions are threefold.\u0000(1) We construct a Neyman-orthogonal score function that remains valid under\u0000measurement error, incorporating a bias correction term to account for\u0000error-induced correlations. (2) We propose a method of moments estimator for\u0000the measurement error variance, enabling implementation without prior knowledge\u0000of the error covariance structure. (3) We establish the $sqrt{N}$-consistency\u0000and asymptotic normality of our estimator under general conditions, allowing\u0000for both the number of covariates and the magnitude of measurement error to\u0000increase with the sample size. Our theoretical results demonstrate the\u0000estimator's efficiency within the class of regularized high-dimensional\u0000estimators accounting for measurement error. Monte Carlo simulations\u0000corroborate our asymptotic theory and illustrate the estimator's robust\u0000performance across various levels of measurement error. Notably, our\u0000covariance-oblivious approach nearly matches the efficiency of methods that\u0000assume known error variance.","PeriodicalId":501293,"journal":{"name":"arXiv - ECON - Econometrics","volume":"67 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184152","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

Inference on Consensus Ranking of Distributions 一致同意的分布排名推论

arXiv - ECON - Econometrics Pub Date : 2024-08-25 DOI: arxiv-2408.13949

David M. Kaplan

引用次数: 0

Cross-sectional Dependence in Idiosyncratic Volatility 非同步波动性的横截面依赖性

arXiv - ECON - Econometrics Pub Date : 2024-08-24 DOI: arxiv-2408.13437

Ilze Kalnina, Kokouvi Tewou

{"title":"Cross-sectional Dependence in Idiosyncratic Volatility","authors":"Ilze Kalnina, Kokouvi Tewou","doi":"arxiv-2408.13437","DOIUrl":"https://doi.org/arxiv-2408.13437","url":null,"abstract":"This paper introduces an econometric framework for analyzing cross-sectional\u0000dependence in the idiosyncratic volatilities of assets using high frequency\u0000data. We first consider the estimation of standard measures of dependence in\u0000the idiosyncratic volatilities such as covariances and correlations. Naive\u0000estimators of these measures are biased due to the use of the error-laden\u0000estimates of idiosyncratic volatilities. We provide bias-corrected estimators\u0000and the relevant asymptotic theory. Next, we introduce an idiosyncratic\u0000volatility factor model, in which we decompose the variation in idiosyncratic\u0000volatilities into two parts: the variation related to the systematic factors\u0000such as the market volatility, and the residual variation. Again, naive\u0000estimators of the decomposition are biased, and we provide bias-corrected\u0000estimators. We also provide the asymptotic theory that allows us to test\u0000whether the residual (non-systematic) components of the idiosyncratic\u0000volatilities exhibit cross-sectional dependence. We apply our methodology to\u0000the S&P 100 index constituents, and document strong cross-sectional dependence\u0000in their idiosyncratic volatilities. We consider two different sets of\u0000idiosyncratic volatility factors, and find that neither can fully account for\u0000the cross-sectional dependence in idiosyncratic volatilities. For each model,\u0000we map out the network of dependencies in residual (non-systematic)\u0000idiosyncratic volatilities across all stocks.","PeriodicalId":501293,"journal":{"name":"arXiv - ECON - Econometrics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184173","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

Machine Learning and the Yield Curve: Tree-Based Macroeconomic Regime Switching 机器学习与收益率曲线：基于树的宏观经济制度转换

arXiv - ECON - Econometrics Pub Date : 2024-08-23 DOI: arxiv-2408.12863

Siyu Bie, Francis X. Diebold, Jingyu He, Junye Li

引用次数: 0

Difference-in-differences with as few as two cross-sectional units -- A new perspective to the democracy-growth debate 只有两个横截面单位的差异 -- 民主与增长辩论的新视角

arXiv - ECON - Econometrics Pub Date : 2024-08-23 DOI: arxiv-2408.13047

Gilles Koumou, Emmanuel Selorm Tsyawo

引用次数: 0

Integrating an agent-based behavioral model in microtransit forecasting and revenue management 在微观交通预测和收入管理中整合基于代理的行为模型

arXiv - ECON - Econometrics Pub Date : 2024-08-22 DOI: arxiv-2408.12577

Xiyuan Ren, Joseph Y. J. Chow, Venktesh Pandey, Linfei Yuan

{"title":"Integrating an agent-based behavioral model in microtransit forecasting and revenue management","authors":"Xiyuan Ren, Joseph Y. J. Chow, Venktesh Pandey, Linfei Yuan","doi":"arxiv-2408.12577","DOIUrl":"https://doi.org/arxiv-2408.12577","url":null,"abstract":"As an IT-enabled multi-passenger mobility service, microtransit has the\u0000potential to improve accessibility, reduce congestion, and enhance flexibility\u0000in transportation options. However, due to its heterogeneous impacts on\u0000different communities and population segments, there is a need for better tools\u0000in microtransit forecast and revenue management, especially when actual usage\u0000data are limited. We propose a novel framework based on an agent-based mixed\u0000logit model estimated with microtransit usage data and synthetic trip data. The\u0000framework involves estimating a lower-branch mode choice model with synthetic\u0000trip data, combining lower-branch parameters with microtransit data to estimate\u0000an upper-branch ride pass subscription model, and applying the nested model to\u0000evaluate microtransit pricing and subsidy policies. The framework enables\u0000further decision-support analysis to consider diverse travel patterns and\u0000heterogeneous tastes of the total population. We test the framework in a case\u0000study with synthetic trip data from Replica Inc. and microtransit data from\u0000Arlington Via. The lower-branch model result in a rho-square value of 0.603 on\u0000weekdays and 0.576 on weekends. Predictions made by the upper-branch model\u0000closely match the marginal subscription data. In a ride pass pricing policy\u0000scenario, we show that a discount in weekly pass (from $25 to $18.9) and\u0000monthly pass (from $80 to $71.5) would surprisingly increase total revenue by\u0000$102/day. In an event- or place-based subsidy policy scenario, we show that a\u0000100% fare discount would reduce 80 car trips during peak hours at AT&T Stadium,\u0000requiring a subsidy of $32,068/year.","PeriodicalId":501293,"journal":{"name":"arXiv - ECON - Econometrics","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184172","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