arXiv - ECON - Econometrics最新文献_第7页

Conditional nonparametric variable screening by neural factor regression 通过神经因子回归进行条件非参数变量筛选

arXiv - ECON - Econometrics Pub Date : 2024-08-20 DOI: arxiv-2408.10825

Jianqing FanPrinceton University, Weining WangUniversity of Groningen, Yue ZhaoUniversity of York

{"title":"Conditional nonparametric variable screening by neural factor regression","authors":"Jianqing FanPrinceton University, Weining WangUniversity of Groningen, Yue ZhaoUniversity of York","doi":"arxiv-2408.10825","DOIUrl":"https://doi.org/arxiv-2408.10825","url":null,"abstract":"High-dimensional covariates often admit linear factor structure. To\u0000effectively screen correlated covariates in high-dimension, we propose a\u0000conditional variable screening test based on non-parametric regression using\u0000neural networks due to their representation power. We ask the question whether\u0000individual covariates have additional contributions given the latent factors or\u0000more generally a set of variables. Our test statistics are based on the\u0000estimated partial derivative of the regression function of the candidate\u0000variable for screening and a observable proxy for the latent factors. Hence,\u0000our test reveals how much predictors contribute additionally to the\u0000non-parametric regression after accounting for the latent factors. Our\u0000derivative estimator is the convolution of a deep neural network regression\u0000estimator and a smoothing kernel. We demonstrate that when the neural network\u0000size diverges with the sample size, unlike estimating the regression function\u0000itself, it is necessary to smooth the partial derivative of the neural network\u0000estimator to recover the desired convergence rate for the derivative. Moreover,\u0000our screening test achieves asymptotic normality under the null after finely\u0000centering our test statistics that makes the biases negligible, as well as\u0000consistency for local alternatives under mild conditions. We demonstrate the\u0000performance of our test in a simulation study and two real world applications.","PeriodicalId":501293,"journal":{"name":"arXiv - ECON - Econometrics","volume":"1587 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184184","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

Gradient Wild Bootstrap for Instrumental Variable Quantile Regressions with Weak and Few Clusters 用于弱聚类和少聚类工具变量量值回归的梯度野生引导法

arXiv - ECON - Econometrics Pub Date : 2024-08-20 DOI: arxiv-2408.10686

Wenjie Wang, Yichong Zhang

{"title":"Gradient Wild Bootstrap for Instrumental Variable Quantile Regressions with Weak and Few Clusters","authors":"Wenjie Wang, Yichong Zhang","doi":"arxiv-2408.10686","DOIUrl":"https://doi.org/arxiv-2408.10686","url":null,"abstract":"We study the gradient wild bootstrap-based inference for instrumental\u0000variable quantile regressions in the framework of a small number of large\u0000clusters in which the number of clusters is viewed as fixed, and the number of\u0000observations for each cluster diverges to infinity. For the Wald inference, we\u0000show that our wild bootstrap Wald test, with or without studentization using\u0000the cluster-robust covariance estimator (CRVE), controls size asymptotically up\u0000to a small error as long as the parameter of endogenous variable is strongly\u0000identified in at least one of the clusters. We further show that the wild\u0000bootstrap Wald test with CRVE studentization is more powerful for distant local\u0000alternatives than that without. Last, we develop a wild bootstrap\u0000Anderson-Rubin (AR) test for the weak-identification-robust inference. We show\u0000it controls size asymptotically up to a small error, even under weak or partial\u0000identification for all clusters. We illustrate the good finite-sample\u0000performance of the new inference methods using simulations and provide an\u0000empirical application to a well-known dataset about US local labor markets.","PeriodicalId":501293,"journal":{"name":"arXiv - ECON - Econometrics","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184183","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

kendallknight: Efficient Implementation of Kendall's Correlation Coefficient Computation kendallknight：肯德尔相关系数计算的高效实现

arXiv - ECON - Econometrics Pub Date : 2024-08-19 DOI: arxiv-2408.09618

Mauricio Vargas Sepúlveda

引用次数: 0

Anytime-Valid Inference for Double/Debiased Machine Learning of Causal Parameters 因果参数双重/偏差机器学习的随时有效推理

arXiv - ECON - Econometrics Pub Date : 2024-08-18 DOI: arxiv-2408.09598

Abhinandan Dalal, Patrick Blöbaum, Shiva Kasiviswanathan, Aaditya Ramdas

{"title":"Anytime-Valid Inference for Double/Debiased Machine Learning of Causal Parameters","authors":"Abhinandan Dalal, Patrick Blöbaum, Shiva Kasiviswanathan, Aaditya Ramdas","doi":"arxiv-2408.09598","DOIUrl":"https://doi.org/arxiv-2408.09598","url":null,"abstract":"Double (debiased) machine learning (DML) has seen widespread use in recent\u0000years for learning causal/structural parameters, in part due to its flexibility\u0000and adaptability to high-dimensional nuisance functions as well as its ability\u0000to avoid bias from regularization or overfitting. However, the classic\u0000double-debiased framework is only valid asymptotically for a predetermined\u0000sample size, thus lacking the flexibility of collecting more data if sharper\u0000inference is needed, or stopping data collection early if useful inferences can\u0000be made earlier than expected. This can be of particular concern in large scale\u0000experimental studies with huge financial costs or human lives at stake, as well\u0000as in observational studies where the length of confidence of intervals do not\u0000shrink to zero even with increasing sample size due to partial identifiability\u0000of a structural parameter. In this paper, we present time-uniform counterparts\u0000to the asymptotic DML results, enabling valid inference and confidence\u0000intervals for structural parameters to be constructed at any arbitrary\u0000(possibly data-dependent) stopping time. We provide conditions which are only\u0000slightly stronger than the standard DML conditions, but offer the stronger\u0000guarantee for anytime-valid inference. This facilitates the transformation of\u0000any existing DML method to provide anytime-valid guarantees with minimal\u0000modifications, making it highly adaptable and easy to use. We illustrate our\u0000procedure using two instances: a) local average treatment effect in online\u0000experiments with non-compliance, and b) partial identification of average\u0000treatment effect in observational studies with potential unmeasured\u0000confounding.","PeriodicalId":501293,"journal":{"name":"arXiv - ECON - Econometrics","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184217","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

Experimental Design For Causal Inference Through An Optimization Lens 通过优化视角进行因果推理的实验设计

arXiv - ECON - Econometrics Pub Date : 2024-08-18 DOI: arxiv-2408.09607

Jinglong Zhao

引用次数: 0

Deep Learning for the Estimation of Heterogeneous Parameters in Discrete Choice Models 离散选择模型中异质参数估计的深度学习

arXiv - ECON - Econometrics Pub Date : 2024-08-18 DOI: arxiv-2408.09560

Stephan Hetzenecker, Maximilian Osterhaus

{"title":"Deep Learning for the Estimation of Heterogeneous Parameters in Discrete Choice Models","authors":"Stephan Hetzenecker, Maximilian Osterhaus","doi":"arxiv-2408.09560","DOIUrl":"https://doi.org/arxiv-2408.09560","url":null,"abstract":"This paper studies the finite sample performance of the flexible estimation\u0000approach of Farrell, Liang, and Misra (2021a), who propose to use deep learning\u0000for the estimation of heterogeneous parameters in economic models, in the\u0000context of discrete choice models. The approach combines the structure imposed\u0000by economic models with the flexibility of deep learning, which assures the\u0000interpretebility of results on the one hand, and allows estimating flexible\u0000functional forms of observed heterogeneity on the other hand. For inference\u0000after the estimation with deep learning, Farrell et al. (2021a) derive an\u0000influence function that can be applied to many quantities of interest. We\u0000conduct a series of Monte Carlo experiments that investigate the impact of\u0000regularization on the proposed estimation and inference procedure in the\u0000context of discrete choice models. The results show that the deep learning\u0000approach generally leads to precise estimates of the true average parameters\u0000and that regular robust standard errors lead to invalid inference results,\u0000showing the need for the influence function approach for inference. Without\u0000regularization, the influence function approach can lead to substantial bias\u0000and large estimated standard errors caused by extreme outliers. Regularization\u0000reduces this property and stabilizes the estimation procedure, but at the\u0000expense of inducing an additional bias. The bias in combination with decreasing\u0000variance associated with increasing regularization leads to the construction of\u0000invalid inferential statements in our experiments. Repeated sample splitting,\u0000unlike regularization, stabilizes the estimation approach without introducing\u0000an additional bias, thereby allowing for the construction of valid inferential\u0000statements.","PeriodicalId":501293,"journal":{"name":"arXiv - ECON - Econometrics","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184186","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

Externally Valid Selection of Experimental Sites via the k-Median Problem 通过 k-Median 问题选择外部有效的实验点

arXiv - ECON - Econometrics Pub Date : 2024-08-17 DOI: arxiv-2408.09187

José Luis Montiel Olea, Brenda Prallon, Chen Qiu, Jörg Stoye, Yiwei Sun

引用次数: 0

Method of Moments Estimation for Affine Stochastic Volatility Models 仿随机波动率模型的矩估计法

arXiv - ECON - Econometrics Pub Date : 2024-08-17 DOI: arxiv-2408.09185

Yan-Feng Wu, Xiangyu Yang, Jian-Qiang Hu

引用次数: 0

Counterfactual and Synthetic Control Method: Causal Inference with Instrumented Principal Component Analysis 反事实和合成控制法：利用工具主成分分析进行因果推断

arXiv - ECON - Econometrics Pub Date : 2024-08-17 DOI: arxiv-2408.09271

Cong Wang

{"title":"Counterfactual and Synthetic Control Method: Causal Inference with Instrumented Principal Component Analysis","authors":"Cong Wang","doi":"arxiv-2408.09271","DOIUrl":"https://doi.org/arxiv-2408.09271","url":null,"abstract":"The fundamental problem of causal inference lies in the absence of\u0000counterfactuals. Traditional methodologies impute the missing counterfactuals\u0000implicitly or explicitly based on untestable or overly stringent assumptions.\u0000Synthetic control method (SCM) utilizes a weighted average of control units to\u0000impute the missing counterfactual for the treated unit. Although SCM relaxes\u0000some strict assumptions, it still requires the treated unit to be inside the\u0000convex hull formed by the controls, avoiding extrapolation. In recent advances,\u0000researchers have modeled the entire data generating process (DGP) to explicitly\u0000impute the missing counterfactual. This paper expands the interactive fixed\u0000effect (IFE) model by instrumenting covariates into factor loadings, adding\u0000additional robustness. This methodology offers multiple benefits: firstly, it\u0000incorporates the strengths of previous SCM approaches, such as the relaxation\u0000of the untestable parallel trends assumption (PTA). Secondly, it does not\u0000require the targeted outcomes to be inside the convex hull formed by the\u0000controls. Thirdly, it eliminates the need for correct model specification\u0000required by the IFE model. Finally, it inherits the ability of principal\u0000component analysis (PCA) to effectively handle high-dimensional data and\u0000enhances the value extracted from numerous covariates.","PeriodicalId":501293,"journal":{"name":"arXiv - ECON - Econometrics","volume":"141 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184214","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

Revisiting the Many Instruments Problem using Random Matrix Theory 利用随机矩阵理论重新审视众多工具问题

arXiv - ECON - Econometrics Pub Date : 2024-08-16 DOI: arxiv-2408.08580

Helmut Farbmacher, Rebecca Groh, Michael Mühlegger, Gabriel Vollert

引用次数: 0