Econometrics: Econometric & Statistical Methods - Special Topics eJournal最新文献

Finite Sample Evaluation of Causal Machine Learning Methods: Guidelines for the Applied Researcher 因果机器学习方法的有限样本评估:应用研究指南

Econometrics: Econometric & Statistical Methods - Special Topics eJournal Pub Date : 2021-10-14 DOI: 10.2139/ssrn.3942461

A. Naghi

{"title":"Finite Sample Evaluation of Causal Machine Learning Methods: Guidelines for the Applied Researcher","authors":"A. Naghi","doi":"10.2139/ssrn.3942461","DOIUrl":"https://doi.org/10.2139/ssrn.3942461","url":null,"abstract":"The econometrics literature proposed several new causal machine learning methods (CML) in the past few years. These methods harness the strength of machine learning methods to flexibly model the relationship between the treatment, outcome and confounders, while providing valid inferential statements. Whereas numerous options are available now to the applied economics researcher, there is limited guidance on the most useful methodology for a particular applied setting. In this paper, we perform a comprehensive evaluation of the finite sample performance of recently introduced CML methods from the econometrics literature, under a wide range of data generating processes. We focus our analysis on data features that are relevant for causal inference such as varying degrees of: nonlinearity in the outcome and treatment equations, overlap, percentage of treated, alignment and heterogeneity in the treatment effect. We evaluate the methods that have received the most attention so far from the empirical economics literature: double machine learning, causal forest and the generic machine learning methods. Results on the bias, root mean squared error, coverage rates and interval lengths for the average treatment effect, group average treatment effects and individual treatment effects reveal information on the characteristics of the methods and the data features that affect their performance the most.","PeriodicalId":139983,"journal":{"name":"Econometrics: Econometric & Statistical Methods - Special Topics eJournal","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126663020","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: 2

A quantum oscillator model of stock markets 股票市场的量子振荡器模型

Econometrics: Econometric & Statistical Methods - Special Topics eJournal Pub Date : 2021-10-12 DOI: 10.2139/ssrn.3941518

D. Orrell

引用次数: 3

Most Claimed Statistical Findings in Cross-Sectional Return Predictability Are Likely True 在横断面收益可预测性中，大多数声称的统计结果可能是正确的

Econometrics: Econometric & Statistical Methods - Special Topics eJournal Pub Date : 2021-10-08 DOI: 10.2139/ssrn.3912915

Andrew Y. Chen

引用次数: 4

Bounding Omitted Variable Bias Using Auxiliary Data 使用辅助数据边界省略变量偏差

Econometrics: Econometric & Statistical Methods - Special Topics eJournal Pub Date : 2021-09-28 DOI: 10.2139/ssrn.3866876

Yu-Ning Hwang

引用次数: 2

Stocking Under Random Demand and Product Variety: Exact Models and Heuristics 随机需求与产品品种下的库存:精确模型与启发式

Econometrics: Econometric & Statistical Methods - Special Topics eJournal Pub Date : 2021-09-15 DOI: 10.1111/poms.13592

Vashkar Ghosh, Anand A. Paul, Lingjiong Zhu

{"title":"Stocking Under Random Demand and Product Variety: Exact Models and Heuristics","authors":"Vashkar Ghosh, Anand A. Paul, Lingjiong Zhu","doi":"10.1111/poms.13592","DOIUrl":"https://doi.org/10.1111/poms.13592","url":null,"abstract":"Efficient inventory management in the face of product variety is an important part of retail operations management. In this paper, we analyze the optimal stocking policy for a retailer, in a setup with a single horizontally differentiated product with an arbitrary number of product variants, stochastic demand, and two-level consumer choice. The demands for individual product variants are negatively correlated conditional on the total demand. We assume that each customer will purchase one unit of a preferred product variant, if it is in stock, and will seek to buy a second choice product, if the former is not in stock. We formulate an exact model, with Poisson customer arrivals. In order to maintain tractability and characterize an optimal policy analytically, we develop a benchmark model which does not explicitly account for the stochastic nature of customer arrival times. In this model, which is a heuristic approximation of the exact model we find simple conditions under which the objective of maximizing expected profit is jointly concave in the stocking levels of the product variants; under these conditions we prove that the optimal stocking levels are simply scaled versions of the optimal newsvendor quantities. We then analytically establish a connection between the exact and benchmark models. We develop a dynamic Monte Carlo simulation to gain further insights on the impact of different performance measures on the effectiveness of the optimal policy in the benchmark model and its performance in reference to the exact optimal policy.","PeriodicalId":139983,"journal":{"name":"Econometrics: Econometric & Statistical Methods - Special Topics eJournal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115922597","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: 2

Forecasting the Effectiveness of the Enterprise to Intensify Innovation and Investment Development, Taking into Account the Financial Component of Economic Potential 考虑经济潜力金融成分的企业加强创新和投资发展的有效性预测

Econometrics: Econometric & Statistical Methods - Special Topics eJournal Pub Date : 2021-08-31 DOI: 10.15587/1729-4061.2021.239249

A. Bradul, Larysa Varava, A. Turylo, I. Dashko, Andrii Varava

{"title":"Forecasting the Effectiveness of the Enterprise to Intensify Innovation and Investment Development, Taking into Account the Financial Component of Economic Potential","authors":"A. Bradul, Larysa Varava, A. Turylo, I. Dashko, Andrii Varava","doi":"10.15587/1729-4061.2021.239249","DOIUrl":"https://doi.org/10.15587/1729-4061.2021.239249","url":null,"abstract":"The study is devoted to the problem of analysis and forecasting of the effectiveness of the results of enterprises to ensure production and economic reserves to intensify innovation and investment development in the context of monitoring the state of their economic potential. It is the basis for the formation of endogenous and exogenous capabilities of the enterprise, aimed at achieving targeted results in each area of its activities. Creating favorable conditions for economic growth of enterprises based on the intensification of innovation and investment development requires the effective implementation of production programs using the financial component of economic potential.\u0000Based on the analysis of methodological tools for evaluating the activities of a mining and processing enterprise, formed a comprehensive methodology for quantitative and qualitative assessment of actual and projected values of performance indicators of the enterprise. It is based on determining the statistical probability of achieving a positive level of the indicator, the probability of its falling into a given interval of deviation from the recommended allowable values and an integrated assessment of the financial component of economic potential.\u0000Approbation of the developed technique is carried out within the limits of the express analysis of effective indicators of efficiency of activity of mining and processing enterprises. The results showed that with high reliability of the forecast (more than 0.85), the recommended values of the level of margin to achieve the desired efficiency are in the range of 8÷10 %. Within a sufficient level of reliability of the forecast (not less than 0.75), the recommended values of this margin are 10÷24 %. In this case, the integrated indicator of the assessment of the financial component should be more than 0.3","PeriodicalId":139983,"journal":{"name":"Econometrics: Econometric & Statistical Methods - Special Topics eJournal","volume":"70 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121013480","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: 3

Equivalence between Mean Independence and Zero Correlation of the Error Term with any Function of the Covariates 误差项与任意协变量函数的均值无关和零相关之间的等价性

Econometrics: Econometric & Statistical Methods - Special Topics eJournal Pub Date : 2021-08-25 DOI: 10.2139/ssrn.3911567

Mark Stater

引用次数: 0

Policy Evaluation and Temporal-Difference Learning in Continuous Time and Space: A Martingale Approach 连续时空中的政策评价与时间差学习:一个鞅方法

Econometrics: Econometric & Statistical Methods - Special Topics eJournal Pub Date : 2021-08-14 DOI: 10.2139/ssrn.3905379

Yanwei Jia, X. Zhou

{"title":"Policy Evaluation and Temporal-Difference Learning in Continuous Time and Space: A Martingale Approach","authors":"Yanwei Jia, X. Zhou","doi":"10.2139/ssrn.3905379","DOIUrl":"https://doi.org/10.2139/ssrn.3905379","url":null,"abstract":"We propose a unified framework to study policy evaluation (PE) and the associated temporal difference (TD) methods for reinforcement learning in continuous time and space. We show that PE is equivalent to maintaining the martingale condition of a process. From this perspective, we find that the mean--square TD error approximates the quadratic variation of the martingale and thus is not a suitable objective for PE. We present two methods to use the martingale characterization for designing PE algorithms. The first one minimizes a ``martingale loss function\", whose solution is proved to be the best approximation of the true value function in the mean--square sense. This method interprets the classical gradient Monte-Carlo algorithm. The second method is based on a system of equations called the ``martingale orthogonality conditions\" with ``test functions''. Solving these equations in different ways recovers various classical TD algorithms, such as TD($lambda$), LSTD, and GTD. Different choices of test functions determine in what sense the resulting solutions approximate the true value function. Moreover, we prove that any convergent time-discretized algorithm converges to its continuous-time counterpart as the mesh size goes to zero. We demonstrate the theoretical results and corresponding algorithms with numerical experiments and applications.","PeriodicalId":139983,"journal":{"name":"Econometrics: Econometric & Statistical Methods - Special Topics eJournal","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121667637","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: 29

Option Return Predictability with Machine Learning and Big Data 机器学习和大数据的期权回报可预测性

Econometrics: Econometric & Statistical Methods - Special Topics eJournal Pub Date : 2021-07-29 DOI: 10.2139/ssrn.3895984

Turan G. Bali, H. Beckmeyer, Mathis Moerke, F. Weigert

引用次数: 16

Application Overview Of Quantum Computing For Gas Turbine Design and Optimisation 量子计算在燃气轮机设计与优化中的应用综述

Econometrics: Econometric & Statistical Methods - Special Topics eJournal Pub Date : 2021-07-22 DOI: 10.2139/ssrn.3891852

A. Thomas Jayachandran

引用次数: 0