arXiv - ECON - Econometrics最新文献_第4页

The Application of Green GDP and Its Impact on Global Economy and Environment: Analysis of GGDP based on SEEA model 绿色 GDP 的应用及其对全球经济和环境的影响：基于 SEEA 模型的 GGDP 分析

arXiv - ECON - Econometrics Pub Date : 2024-09-04 DOI: arxiv-2409.02642

Mingpu Ma

{"title":"The Application of Green GDP and Its Impact on Global Economy and Environment: Analysis of GGDP based on SEEA model","authors":"Mingpu Ma","doi":"arxiv-2409.02642","DOIUrl":"https://doi.org/arxiv-2409.02642","url":null,"abstract":"This paper presents an analysis of Green Gross Domestic Product (GGDP) using\u0000the System of Environmental-Economic Accounting (SEEA) model to evaluate its\u0000impact on global climate mitigation and economic health. GGDP is proposed as a\u0000superior measure to tradi-tional GDP by incorporating natural resource\u0000consumption, environmental pollution control, and degradation factors. The\u0000study develops a GGDP model and employs grey correlation analysis and grey\u0000prediction models to assess its relationship with these factors. Key findings\u0000demonstrate that replacing GDP with GGDP can positively influence climate\u0000change, partic-ularly in reducing CO2 emissions and stabilizing global\u0000temperatures. The analysis further explores the implications of GGDP adoption\u0000across developed and developing countries, with specific predictions for China\u0000and the United States. The results indicate a potential increase in economic\u0000levels for developing countries, while developed nations may experi-ence a\u0000decrease. Additionally, the shift to GGDP is shown to significantly reduce\u0000natural re-source depletion and population growth rates in the United States,\u0000suggesting broader envi-ronmental and economic benefits. This paper highlights\u0000the universal applicability of the GGDP model and its potential to enhance\u0000environmental and economic policies globally.","PeriodicalId":501293,"journal":{"name":"arXiv - ECON - Econometrics","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184139","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

Distribution Regression Difference-In-Differences 分布回归差异

arXiv - ECON - Econometrics Pub Date : 2024-09-03 DOI: arxiv-2409.02311

Iván Fernández-Val, Jonas Meier, Aico van Vuuren, Francis Vella

引用次数: 0

Variable selection in convex nonparametric least squares via structured Lasso: An application to the Swedish electricity market 通过结构化 Lasso 在凸非参数最小二乘法中选择变量：瑞典电力市场的应用

arXiv - ECON - Econometrics Pub Date : 2024-09-03 DOI: arxiv-2409.01911

Zhiqiang Liao

{"title":"Variable selection in convex nonparametric least squares via structured Lasso: An application to the Swedish electricity market","authors":"Zhiqiang Liao","doi":"arxiv-2409.01911","DOIUrl":"https://doi.org/arxiv-2409.01911","url":null,"abstract":"We study the problem of variable selection in convex nonparametric least\u0000squares (CNLS). Whereas the least absolute shrinkage and selection operator\u0000(Lasso) is a popular technique for least squares, its variable selection\u0000performance is unknown in CNLS problems. In this work, we investigate the\u0000performance of the Lasso CNLS estimator and find out it is usually unable to\u0000select variables efficiently. Exploiting the unique structure of the\u0000subgradients in CNLS, we develop a structured Lasso by combining $ell_1$-norm\u0000and $ell_{infty}$-norm. To improve its predictive performance, we propose a\u0000relaxed version of the structured Lasso where we can control the two\u0000effects--variable selection and model shrinkage--using an additional tuning\u0000parameter. A Monte Carlo study is implemented to verify the finite sample\u0000performances of the proposed approaches. In the application of Swedish\u0000electricity distribution networks, when the regression model is assumed to be\u0000semi-nonparametric, our methods are extended to the doubly penalized CNLS\u0000estimators. The results from the simulation and application confirm that the\u0000proposed structured Lasso performs favorably, generally leading to sparser and\u0000more accurate predictive models, relative to the other variable selection\u0000methods in the literature.","PeriodicalId":501293,"journal":{"name":"arXiv - ECON - Econometrics","volume":"141 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184145","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

Double Machine Learning at Scale to Predict Causal Impact of Customer Actions 规模化双重机器学习预测客户行为的因果影响

arXiv - ECON - Econometrics Pub Date : 2024-09-03 DOI: arxiv-2409.02332

Sushant More, Priya Kotwal, Sujith Chappidi, Dinesh Mandalapu, Chris Khawand

{"title":"Double Machine Learning at Scale to Predict Causal Impact of Customer Actions","authors":"Sushant More, Priya Kotwal, Sujith Chappidi, Dinesh Mandalapu, Chris Khawand","doi":"arxiv-2409.02332","DOIUrl":"https://doi.org/arxiv-2409.02332","url":null,"abstract":"Causal Impact (CI) of customer actions are broadly used across the industry\u0000to inform both short- and long-term investment decisions of various types. In\u0000this paper, we apply the double machine learning (DML) methodology to estimate\u0000the CI values across 100s of customer actions of business interest and 100s of\u0000millions of customers. We operationalize DML through a causal ML library based\u0000on Spark with a flexible, JSON-driven model configuration approach to estimate\u0000CI at scale (i.e., across hundred of actions and millions of customers). We\u0000outline the DML methodology and implementation, and associated benefits over\u0000the traditional potential outcomes based CI model. We show population-level as\u0000well as customer-level CI values along with confidence intervals. The\u0000validation metrics show a 2.2% gain over the baseline methods and a 2.5X gain\u0000in the computational time. Our contribution is to advance the scalable\u0000application of CI, while also providing an interface that allows faster\u0000experimentation, cross-platform support, ability to onboard new use cases, and\u0000improves accessibility of underlying code for partner teams.","PeriodicalId":501293,"journal":{"name":"arXiv - ECON - Econometrics","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184142","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

Double Machine Learning meets Panel Data -- Promises, Pitfalls, and Potential Solutions 双机器学习与面板数据 -- 前景、陷阱和潜在解决方案

arXiv - ECON - Econometrics Pub Date : 2024-09-02 DOI: arxiv-2409.01266

Jonathan Fuhr, Dominik Papies

{"title":"Double Machine Learning meets Panel Data -- Promises, Pitfalls, and Potential Solutions","authors":"Jonathan Fuhr, Dominik Papies","doi":"arxiv-2409.01266","DOIUrl":"https://doi.org/arxiv-2409.01266","url":null,"abstract":"Estimating causal effect using machine learning (ML) algorithms can help to\u0000relax functional form assumptions if used within appropriate frameworks.\u0000However, most of these frameworks assume settings with cross-sectional data,\u0000whereas researchers often have access to panel data, which in traditional\u0000methods helps to deal with unobserved heterogeneity between units. In this\u0000paper, we explore how we can adapt double/debiased machine learning (DML)\u0000(Chernozhukov et al., 2018) for panel data in the presence of unobserved\u0000heterogeneity. This adaptation is challenging because DML's cross-fitting\u0000procedure assumes independent data and the unobserved heterogeneity is not\u0000necessarily additively separable in settings with nonlinear observed\u0000confounding. We assess the performance of several intuitively appealing\u0000estimators in a variety of simulations. While we find violations of the\u0000cross-fitting assumptions to be largely inconsequential for the accuracy of the\u0000effect estimates, many of the considered methods fail to adequately account for\u0000the presence of unobserved heterogeneity. However, we find that using\u0000predictive models based on the correlated random effects approach (Mundlak,\u00001978) within DML leads to accurate coefficient estimates across settings, given\u0000a sample size that is large relative to the number of observed confounders. We\u0000also show that the influence of the unobserved heterogeneity on the observed\u0000confounders plays a significant role for the performance of most alternative\u0000methods.","PeriodicalId":501293,"journal":{"name":"arXiv - ECON - Econometrics","volume":"1583 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184144","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

Bandit Algorithms for Policy Learning: Methods, Implementation, and Welfare-performance 政策学习的 Bandit 算法：方法、实施和福利绩效

arXiv - ECON - Econometrics Pub Date : 2024-08-31 DOI: arxiv-2409.00379

Toru Kitagawa, Jeff Rowley

{"title":"Bandit Algorithms for Policy Learning: Methods, Implementation, and Welfare-performance","authors":"Toru Kitagawa, Jeff Rowley","doi":"arxiv-2409.00379","DOIUrl":"https://doi.org/arxiv-2409.00379","url":null,"abstract":"Static supervised learning-in which experimental data serves as a training\u0000sample for the estimation of an optimal treatment assignment policy-is a\u0000commonly assumed framework of policy learning. An arguably more realistic but\u0000challenging scenario is a dynamic setting in which the planner performs\u0000experimentation and exploitation simultaneously with subjects that arrive\u0000sequentially. This paper studies bandit algorithms for learning an optimal\u0000individualised treatment assignment policy. Specifically, we study\u0000applicability of the EXP4.P (Exponential weighting for Exploration and\u0000Exploitation with Experts) algorithm developed by Beygelzimer et al. (2011) to\u0000policy learning. Assuming that the class of policies has a finite\u0000Vapnik-Chervonenkis dimension and that the number of subjects to be allocated\u0000is known, we present a high probability welfare-regret bound of the algorithm.\u0000To implement the algorithm, we use an incremental enumeration algorithm for\u0000hyperplane arrangements. We perform extensive numerical analysis to assess the\u0000algorithm's sensitivity to its tuning parameters and its welfare-regret\u0000performance. Further simulation exercises are calibrated to the National Job\u0000Training Partnership Act (JTPA) Study sample to determine how the algorithm\u0000performs when applied to economic data. Our findings highlight various\u0000computational challenges and suggest that the limited welfare gain from the\u0000algorithm is due to substantial heterogeneity in causal effects in the JTPA\u0000data.","PeriodicalId":501293,"journal":{"name":"arXiv - ECON - Econometrics","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184143","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

Weighted Regression with Sybil Networks 利用 Sybil 网络进行加权回归

arXiv - ECON - Econometrics Pub Date : 2024-08-30 DOI: arxiv-2408.17426

Nihar Shah

引用次数: 0

State Space Model of Realized Volatility under the Existence of Dependent Market Microstructure Noise 存在依赖性市场微观结构噪声时的已实现波动率状态空间模型

arXiv - ECON - Econometrics Pub Date : 2024-08-30 DOI: arxiv-2408.17187

Toru Yano

引用次数: 0

Sensitivity Analysis for Dynamic Discrete Choice Models 动态离散选择模型的敏感性分析

arXiv - ECON - Econometrics Pub Date : 2024-08-29 DOI: arxiv-2408.16330

Chun Pong Lau

引用次数: 0

Marginal homogeneity tests with panel data 面板数据的边际同质性检验

arXiv - ECON - Econometrics Pub Date : 2024-08-28 DOI: arxiv-2408.15862

Federico Bugni, Jackson Bunting, Muyang Ren

{"title":"Marginal homogeneity tests with panel data","authors":"Federico Bugni, Jackson Bunting, Muyang Ren","doi":"arxiv-2408.15862","DOIUrl":"https://doi.org/arxiv-2408.15862","url":null,"abstract":"A panel dataset satisfies marginal homogeneity if the time-specific marginal\u0000distributions are homogeneous or time-invariant. Marginal homogeneity is\u0000relevant in economic settings such as dynamic discrete games. In this paper, we\u0000propose several tests for the hypothesis of marginal homogeneity and\u0000investigate their properties. We consider an asymptotic framework in which the\u0000number of individuals n in the panel diverges, and the number of periods T is\u0000fixed. We implement our tests by comparing a studentized or non-studentized\u0000T-sample version of the Cramer-von Mises statistic with a suitable critical\u0000value. We propose three methods to construct the critical value: asymptotic\u0000approximations, the bootstrap, and time permutations. We show that the first\u0000two methods result in asymptotically exact hypothesis tests. The permutation\u0000test based on a non-studentized statistic is asymptotically exact when T=2, but\u0000is asymptotically invalid when T>2. In contrast, the permutation test based on\u0000a studentized statistic is always asymptotically exact. Finally, under a\u0000time-exchangeability assumption, the permutation test is exact in finite\u0000samples, both with and without studentization.","PeriodicalId":501293,"journal":{"name":"arXiv - ECON - Econometrics","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184149","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