ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic)最新文献_第4页

A Discussion of Non-Gaussian Price Processes for Energy and Commodity Operations 能源和商品操作的非高斯价格过程的讨论

ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic) Pub Date : 2020-06-18 DOI: 10.2139/ssrn.3305438

A. Gambaro, N. Secomandi

引用次数: 0

Modelling GDP for Sudan using ARIMA 使用ARIMA对苏丹的GDP进行建模

ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic) Pub Date : 2020-06-18 DOI: 10.2139/ssrn.3630099

H. M. Hassan

引用次数: 3

Backward Deep BSDE Methods and Applications to Nonlinear Problems 后向深度BSDE方法及其在非线性问题中的应用

ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic) Pub Date : 2020-06-13 DOI: 10.2139/ssrn.3626208

Jessica (Yajie) Yu, B. Hientzsch, N. Ganesan

引用次数: 9

Dynamic Network Risk 动态网络风险

ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic) Pub Date : 2020-06-08 DOI: 10.2139/ssrn.3622200

M. Ellington, Jozef Baruník

引用次数: 5

Arbitrary Spearman’s Rank Correlations in Maximum Entropy Bootstrap and Improved Monte Carlo Simulations 最大熵Bootstrap中的任意Spearman秩相关及改进的蒙特卡罗模拟

ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic) Pub Date : 2020-06-07 DOI: 10.2139/ssrn.3621614

H. Vinod, Fred Viole

引用次数: 0

Swag: A Wrapper Method for Sparse Learning Swag:稀疏学习的包装方法

ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic) Pub Date : 2020-06-01 DOI: 10.2139/ssrn.3633843

R. Molinari, Gaetan Bakalli, S. Guerrier, Cesare Miglioli, Samuel Orso, O. Scaillet

{"title":"Swag: A Wrapper Method for Sparse Learning","authors":"R. Molinari, Gaetan Bakalli, S. Guerrier, Cesare Miglioli, Samuel Orso, O. Scaillet","doi":"10.2139/ssrn.3633843","DOIUrl":"https://doi.org/10.2139/ssrn.3633843","url":null,"abstract":"Predictive power has always been the main research focus of learning algorithms. While the general approach for these algorithms is to consider all possible attributes in a dataset to best predict the response of interest, an important branch of research is focused on sparse learning. Indeed, in many practical settings we believe that only an extremely small combination of different attributes affect the response. However even sparse-learning methods can still preserve a high number of attributes in high-dimensional settings and possibly deliver inconsistent prediction performance. The latter methods can also be hard to interpret for researchers and practitioners, a problem which is even more relevant for the ``black-box''-type mechanisms of many learning approaches. Finally, there is often a problem of replicability since not all data-collection procedures measure (or observe) the same attributes and therefore cannot make use of proposed learners for testing purposes. To address all the previous issues, we propose to study a procedure that combines screening and wrapper methods and aims to find a library of extremely low-dimensional attribute combinations (with consequent low data collection and storage costs) in order to (i) match or improve the predictive performance of any particular learning method which uses all attributes as an input (including sparse learners); (ii) provide a low-dimensional network of attributes easily interpretable by researchers and practitioners; and (iii) increase the potential replicability of results due to a diversity of attribute combinations defining strong learners with equivalent predictive power. We call this algorithm ``Sparse Wrapper AlGorithm'' (SWAG).","PeriodicalId":239853,"journal":{"name":"ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic)","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116207172","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: 4

Микроэкономический анализ инвестиционных стратегий зарубежных компаний в России в контексте их внешнеторговой деятельности (Microeconomic Analysis of Investment Strategies of Foreign Companies in Russia in the Context of Their Foreign Trade Activities)

ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic) Pub Date : 2020-05-25 DOI: 10.2139/ssrn.3710580

Alexander Knobel, Yury Zaitsev

{"title":"Микроэкономический анализ инвестиционных стратегий зарубежных компаний в России в контексте их внешнеторговой деятельности (Microeconomic Analysis of Investment Strategies of Foreign Companies in Russia in the Context of Their Foreign Trade Activities)","authors":"Alexander Knobel, Yury Zaitsev","doi":"10.2139/ssrn.3710580","DOIUrl":"https://doi.org/10.2139/ssrn.3710580","url":null,"abstract":"Russian Abstract: Данное исследование посвящено выявлению закономерностей пространственного и отраслевого распределения предприятий прямого иностранного инвестирования в Российской Федерации и выработке рекомендаций для оптимизации стратегии привлечения прямых иностранных инвестиций в Российскую Федерацию. Были применены следующие методы: метод макроэкономического моделирования, оценки эконометрических моделей, а также логического, системного, сравнительного, экономико-статистического анализа. Статистические данные на уровне предприятий использованы из базы данных «РУСЛАНА» и «СПАРК-ИНТЕРФАКС». Результаты позволяют указать на некоторые характерные черты пространственного распределения иностранных предприятий, которые необходимо учитывать при формировании картины предпочтений иностранных инвесторов и политики привлечения иностранных инвесторов в российские регионы. English Abstract: This working paper is devoted to identifying patterns of spatial and sectoral distribution of foreign direct investment enterprises in the Russian Federation and to developing recommendations for optimizing the strategy of attracting foreign direct investment to the Russian Federation. The following methods were applied: the method of macroeconomic modeling, evaluation of econometric models, as well as logical, systemic, comparative, economic and statistical analysis. Statistical data at the enterprise level were used from the «RUSLANA» and «SPARK-INTERFAX» databases. The results allow to point out some characteristic features of the spatial distribution of foreign enterprises, which must be taken into account when forming a picture of preferences of foreign investors and the policy of attracting foreign investors to Russian regions.","PeriodicalId":239853,"journal":{"name":"ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133718077","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

Econometrics Meets Sentiment: An Overview of Methodology and Applications 计量经济学满足情感:方法论和应用概述

ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic) Pub Date : 2020-05-21 DOI: 10.2139/ssrn.2652876

Andres Algaba, David Ardia, Keven Bluteau, S. Borms, Kris Boudt

引用次数: 55

A New Robust Inference for Predictive Quantile Regression 预测分位数回归的一种新的鲁棒推断

ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic) Pub Date : 2020-05-05 DOI: 10.2139/ssrn.3593817

Z. Cai, Hai-qiang Chen, Xiaosai Liao

{"title":"A New Robust Inference for Predictive Quantile Regression","authors":"Z. Cai, Hai-qiang Chen, Xiaosai Liao","doi":"10.2139/ssrn.3593817","DOIUrl":"https://doi.org/10.2139/ssrn.3593817","url":null,"abstract":"For predictive quantile regressions with highly persistent regressors, a conventional test statistic suffers from a serious size distortion and its limiting distribution relies on the unknown persistence degree of predictors. This paper proposes a double-weighted approach to offer a robust inferential theory across all types of persistent regressors. We first estimate a quantile regression with an auxiliary regressor, which is generated as a weighted combination of an exogenous random walk process and a bounded transformation of the original regressor. With a similar spirit of rotation in factor analysis, one can then construct a weighted estimator using the estimated coefficients of the original predictor and the auxiliary regressor. Under some mild conditions, it shows that the self-normalized test statistic based on the weighted estimator converges to a standard normal distribution. Our new approach enjoys a nice property that it can reach the local power under the optimal rate T with nonstationary predictor and squared root of T for stationary predictor, respectively. More importantly, our approach can be easily used to characterize mixed persistence degrees in multiple regressions. Simulations and empirical studies are provided to demonstrate the effectiveness of the newly proposed approach. The heterogenous predictability of US stock returns at different quantile levels is reexamined.","PeriodicalId":239853,"journal":{"name":"ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125374809","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: 10

Construal Level Research in Decision Making: Analysis and Pushing Forward the Debate Using Bibliometric Review and Thematic Analysis 决策中的解释水平研究:运用文献计量学回顾和主题分析进行分析并推动辩论

ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic) Pub Date : 2020-05-01 DOI: 10.37625/abr.23.1.106-135

A. Mishra, A. Raj, A. Pani

{"title":"Construal Level Research in Decision Making: Analysis and Pushing Forward the Debate Using Bibliometric Review and Thematic Analysis","authors":"A. Mishra, A. Raj, A. Pani","doi":"10.37625/abr.23.1.106-135","DOIUrl":"https://doi.org/10.37625/abr.23.1.106-135","url":null,"abstract":"This study examines the extant literature on Construal Level Theory through bibliometric analysis that traces the path of research from 1998 to November 2019. It uses the Scopus database to identify emerging trends, seminal and most-cited papers, authors, universities, and countries that contributed to the development of the theory. A total of 680 papers from 1445 authors, were published in as many as 322 journals. The results indicate that ‘Journal of Experimental Social Psychology,’ ‘Journal of Personality and Social Psychology,’ and ‘Personality and Social Psychology Bulletin,’ were the three most productive sources of knowledge for this theory. The results show that over time, the discussion has progressed from theory to application in different areas of decision sciences, psychology, and management with a recent trend towards application in sustainability. This is the first literature review that has been conducted on the Construal Level Theory using bibliometric analysis. This study attempts to describe, explore possibilities, and provide a roadmap for future research in this field.","PeriodicalId":239853,"journal":{"name":"ERN: Other Econometrics: Econometric & Statistical Methods - Special Topics (Topic)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123670523","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