We propose novel conditional autoregressive Wishart (CAW) models for high-dimensional realized covariance matrices of asset returns. We incorporate measurement errors into realized variance dynamics of Realized Dynamic Conditional Correlation (Re-DCC) model. It is well known that the measurement errors make the realized volatility less persistent than the latent volatility process. Therefore, by introducing measurement errors into realized volatility, the persistence of the realized volatility based on the magnitude of the corresponding measurement errors can be incorporated into the multivariate model. Our empirical analysis performs in- and out-of-sample evaluations for 100 stocks on the Tokyo Stock Exchange from January 1, 2014, through December 31, 2020. Our model based on logarithmic realized volatility shows the best forecast performance across test periods and loss functions.
{"title":"Constructing a Realized DCC Model with Measurement Errors","authors":"Hideto Shigemoto, Takayuki Morimoto","doi":"10.2139/ssrn.3817246","DOIUrl":"https://doi.org/10.2139/ssrn.3817246","url":null,"abstract":"We propose novel conditional autoregressive Wishart (CAW) models for high-dimensional realized covariance matrices of asset returns. We incorporate measurement errors into realized variance dynamics of Realized Dynamic Conditional Correlation (Re-DCC) model. It is well known that the measurement errors make the realized volatility less persistent than the latent volatility process. Therefore, by introducing measurement errors into realized volatility, the persistence of the realized volatility based on the magnitude of the corresponding measurement errors can be incorporated into the multivariate model. Our empirical analysis performs in- and out-of-sample evaluations for 100 stocks on the Tokyo Stock Exchange from January 1, 2014, through December 31, 2020. Our model based on logarithmic realized volatility shows the best forecast performance across test periods and loss functions.","PeriodicalId":320844,"journal":{"name":"PSN: Econometrics","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123116118","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}
Vietnamese Abstract: Đa phần các nghiên cứu tại Việt Nam sử dụng hàm sản xuất Cobb-Douglas và các biến thể của nó như một công cụ trong phân tích và dự báo kinh tế. Dạng hàm này có nhược điểm là các tiền đề của nó quá cứng nhắc mà không phù hợp với hiện thực trong nhiều trường hợp, đặc biệt trong phân tích động thái tăng trưởng kinh tế. Hàm CES (constant elasticity of substitution) với tiền đề linh hoạt hơn, cụ thể là hệ số co giãn giữa vốn và lao động khác một, được sử dụng ngày càng phổ biến. Do vậy, nghiên cứu này được thực hiện nhằm định dạng hàm CES dựa trên dữ liệu của các doanh nghiệp phi tài chính niêm yết trên thị trường chứng khoán Việt Nam. Bằng việc sử dụng phương pháp hồi quy phi tuyến tính Bayes thông qua thuật toán lấy mẫu Random-walk Metropolis Hastings (MH), tác giả khám phá rằng, hàm sản xuất được ước lượng cho các doanh nghiệp phi tài chính Việt Nam là dạng hàm CES với hệ số co giãn thay thế giữa vốn và lao động nhỏ hơn một, tức là lao động và vốn bổ sung nhau. Phát hiện này cho thấy nền kinh tế Việt Nam có thế đối diện với đa trạng thái dừng (multiple steady-states) và các bẫy nghèo đói (poverty traps) nên chưa đạt được các khả năng cho tăng trưởng nội sinh (endogenous growth).
English Abstract: Most studies in Vietnam use the Cobb-Douglas production function and its variants as a tool in economic analysis and forecasting. A great disadvantage of this functional form is that its propositions are extremely rigid, which is not in agreement with reality in many cases, especially in analyzing economic dynamics. The CES (constant elasticity of substitution)function with more flexible propositions, namely the elasticity of substitution between capital and labor different from unity, is used more and more popularly. Therefore, this study was conducted to specify a CES function based on data of non-financial businesses listed on Vietnam's stock market. By using a Bayesian non-linear regression through the Random-walk Metropolis Hastings (MH) sampling algorithm, the author finds that the estimated production function for Vietnamese non-financial enterprises is a CES one with the elasticity of substitution between capital and labor smaller than one. This finding shows that Vietnam's economy might face multiple steady-states and poverty traps, so it has not yet reached the possibilities of endogenous growth
{"title":"Định dạng hàm sản xuất CES bằng phương pháp hồi qui phi tuyến tính Bayes (Specifying a CES Production Function Using a Bayesian Non-linear Regression)","authors":"Thach Nguyen Ngoc","doi":"10.2139/ssrn.3812451","DOIUrl":"https://doi.org/10.2139/ssrn.3812451","url":null,"abstract":"<b>Vietnamese Abstract:</b> Đa phần các nghiên cứu tại Việt Nam sử dụng hàm sản xuất Cobb-Douglas và các biến thể của nó như một công cụ trong phân tích và dự báo kinh tế. Dạng hàm này có nhược điểm là các tiền đề của nó quá cứng nhắc mà không phù hợp với hiện thực trong nhiều trường hợp, đặc biệt trong phân tích động thái tăng trưởng kinh tế. Hàm CES (constant elasticity of substitution) với tiền đề linh hoạt hơn, cụ thể là hệ số co giãn giữa vốn và lao động khác một, được sử dụng ngày càng phổ biến. Do vậy, nghiên cứu này được thực hiện nhằm định dạng hàm CES dựa trên dữ liệu của các doanh nghiệp phi tài chính niêm yết trên thị trường chứng khoán Việt Nam. Bằng việc sử dụng phương pháp hồi quy phi tuyến tính Bayes thông qua thuật toán lấy mẫu Random-walk Metropolis Hastings (MH), tác giả khám phá rằng, hàm sản xuất được ước lượng cho các doanh nghiệp phi tài chính Việt Nam là dạng hàm CES với hệ số co giãn thay thế giữa vốn và lao động nhỏ hơn một, tức là lao động và vốn bổ sung nhau. Phát hiện này cho thấy nền kinh tế Việt Nam có thế đối diện với đa trạng thái dừng (multiple steady-states) và các bẫy nghèo đói (poverty traps) nên chưa đạt được các khả năng cho tăng trưởng nội sinh (endogenous growth).<br><br><b>English Abstract:</b> Most studies in Vietnam use the Cobb-Douglas production function and its variants as a tool in economic analysis and forecasting. A great disadvantage of this functional form is that its propositions are extremely rigid, which is not in agreement with reality in many cases, especially in analyzing economic dynamics. The CES (constant elasticity of substitution)function with more flexible propositions, namely the elasticity of substitution between capital and labor different from unity, is used more and more popularly. Therefore, this study was conducted to specify a CES function based on data of non-financial businesses listed on Vietnam's stock market. By using a Bayesian non-linear regression through the Random-walk Metropolis Hastings (MH) sampling algorithm, the author finds that the estimated production function for Vietnamese non-financial enterprises is a CES one with the elasticity of substitution between capital and labor smaller than one. This finding shows that Vietnam's economy might face multiple steady-states and poverty traps, so it has not yet reached the possibilities of endogenous growth","PeriodicalId":320844,"journal":{"name":"PSN: Econometrics","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123114439","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}
The faster an organization is able to make decisions, the better capable an organization it is—but that requires that information transit the organization quicker. If so, organizations should be structured with a form ideally capable of transmitting information. I propose a method for measuring the closeness of a real-world organization to the ideal form.
{"title":"The Distribution of Emails In An Organization As A Proxy for Information Flow","authors":"D. Lane","doi":"10.2139/ssrn.3776905","DOIUrl":"https://doi.org/10.2139/ssrn.3776905","url":null,"abstract":"The faster an organization is able to make decisions, the better capable an organization it is—but that requires that information transit the organization quicker. If so, organizations should be structured with a form ideally capable of transmitting information. I propose a method for measuring the closeness of a real-world organization to the ideal form.","PeriodicalId":320844,"journal":{"name":"PSN: Econometrics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130723327","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}
Minji Bang, Wayne Yuan Gao, Andrew Postlewaite, Holger Sieg
This paper develops a new method for identifying econometric models with partially latent covariates. Such data structures arise naturally in industrial organization and labor economics settings where data are collected using an "input-based sampling" strategy, e.g., if the sampling unit is one of multiple labor input factors. We show that the latent covariates can be nonparametrically identified, if they are functions of a common shock satisfying some plausible monotonicity assumptions. With the latent covariates identified, semiparametric estimation of the outcome equation proceeds within a standard IV framework that accounts for the endogeneity of the covariates. We illustrate the usefulness of our method using two applications. The first focuses on pharmacies: we find that production function differences between chains and independent pharmacies may partially explain the observed transformation of the industry structure. Our second application investigates education achievement functions and illustrates important differences in child investments between married and divorced couples.
{"title":"Using Monotonicity Restrictions to Identify Models with Partially Latent Covariates","authors":"Minji Bang, Wayne Yuan Gao, Andrew Postlewaite, Holger Sieg","doi":"10.2139/ssrn.3765884","DOIUrl":"https://doi.org/10.2139/ssrn.3765884","url":null,"abstract":"This paper develops a new method for identifying econometric models with partially latent covariates. Such data structures arise naturally in industrial organization and labor economics settings where data are collected using an \"input-based sampling\" strategy, e.g., if the sampling unit is one of multiple labor input factors. We show that the latent covariates can be nonparametrically identified, if they are functions of a common shock satisfying some plausible monotonicity assumptions. With the latent covariates identified, semiparametric estimation of the outcome equation proceeds within a standard IV framework that accounts for the endogeneity of the covariates. We illustrate the usefulness of our method using two applications. The first focuses on pharmacies: we find that production function differences between chains and independent pharmacies may partially explain the observed transformation of the industry structure. Our second application investigates education achievement functions and illustrates important differences in child investments between married and divorced couples.","PeriodicalId":320844,"journal":{"name":"PSN: Econometrics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125893785","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}
The current polarization of elites in the U.S., particularly in Congress, is frequently ascribed to the emergence of cohorts of ideologically extreme legislators replacing moderate ones. Politicians, however, do not operate as isolated agents, driven solely by their preferences. They act within organized parties, whose leaders exert control over the rank-and-file, directing support for and against policies. This paper shows that the omission of party discipline as a driver of political polarization is consequential for our understanding of this phenomenon. We present a multi-dimensional voting model and identification strategy designed to decouple the ideological preferences of lawmakers from the control exerted by their party leadership. Applying this structural framework to the U.S. Congress between 1927-2018, we find that the influence of leaders over their rank-and-file has been a growing driver of polarization in voting, particularly since the 1970s. In 2018, party discipline accounts for around 65% of the polarization in roll call voting. Our findings qualify the interpretation of – and in some cases subvert – a number of empirical claims in the literature that measures polarization with models that lack a formal role for party organizations.
{"title":"Political Parties as Drivers of U.S. Polarization: 1927-2018","authors":"N. Canen, Chad Kendall, Francesco Trebbi","doi":"10.2139/ssrn.3803669","DOIUrl":"https://doi.org/10.2139/ssrn.3803669","url":null,"abstract":"The current polarization of elites in the U.S., particularly in Congress, is frequently ascribed to the emergence of cohorts of ideologically extreme legislators replacing moderate ones. Politicians, however, do not operate as isolated agents, driven solely by their preferences. They act within organized parties, whose leaders exert control over the rank-and-file, directing support for and against policies. This paper shows that the omission of party discipline as a driver of political polarization is consequential for our understanding of this phenomenon. We present a multi-dimensional voting model and identification strategy designed to decouple the ideological preferences of lawmakers from the control exerted by their party leadership. Applying this structural framework to the U.S. Congress between 1927-2018, we find that the influence of leaders over their rank-and-file has been a growing driver of polarization in voting, particularly since the 1970s. In 2018, party discipline accounts for around 65% of the polarization in roll call voting. Our findings qualify the interpretation of – and in some cases subvert – a number of empirical claims in the literature that measures polarization with models that lack a formal role for party organizations.","PeriodicalId":320844,"journal":{"name":"PSN: Econometrics","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132556613","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}
This paper provides a selective overview on the recent development of factor models and their applications in econometric learning. We focus on the perspective of the low-rank structure of factor models, and particularly draws attentions to estimating the model from the low-rank recovery point of view. The survey mainly consists of three parts: the first part is a review on new factor estimations based on modern techniques on recovering low-rank structures of high-dimensional models. The second part discusses statistical inferences of several factor-augmented models and applications in statistical learning models. The final part summarizes new developments dealing with unbalanced panels from the matrix completion perspective.
{"title":"Recent Developments on Factor Models and its Applications in Econometric Learning","authors":"Jianqing Fan, Kunpeng Li, Yuan Liao","doi":"10.2139/ssrn.3695658","DOIUrl":"https://doi.org/10.2139/ssrn.3695658","url":null,"abstract":"This paper provides a selective overview on the recent development of factor models and their applications in econometric learning. We focus on the perspective of the low-rank structure of factor models, and particularly draws attentions to estimating the model from the low-rank recovery point of view. The survey mainly consists of three parts: the first part is a review on new factor estimations based on modern techniques on recovering low-rank structures of high-dimensional models. The second part discusses statistical inferences of several factor-augmented models and applications in statistical learning models. The final part summarizes new developments dealing with unbalanced panels from the matrix completion perspective.<br>","PeriodicalId":320844,"journal":{"name":"PSN: Econometrics","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123801273","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}
We present a new modelling framework for the bi-variate hidden Markov model. The proposed specification is composed by five latent Markovian chains which drive the evolution of the parameters of a bi-variate Gaussian distribution. The maximum likelihood estimator is computed via an expectation conditional maximization algorithm with closed form conditional maximization steps, specifically developed for our model. Identification of model parameters, as well as consistency and asymptotic Normality of the maximum likelihood estimator are discussed. Finite sample properties of the estimator are investigated in an extensive simulation study. An empirical application with the bi-variate series of US stocks and bond returns illustrates the benefits of the new specification with respect to the standard hidden Markov model.
{"title":"Multiple Chains Hidden Markov Models for Bivariate Dynamical Systems","authors":"Leopoldo Catania","doi":"10.2139/ssrn.3662346","DOIUrl":"https://doi.org/10.2139/ssrn.3662346","url":null,"abstract":"We present a new modelling framework for the bi-variate hidden Markov model. The proposed specification is composed by five latent Markovian chains which drive the evolution of the parameters of a bi-variate Gaussian distribution. The maximum likelihood estimator is computed via an expectation conditional maximization algorithm with closed form conditional maximization steps, specifically developed for our model. Identification of model parameters, as well as consistency and asymptotic Normality of the maximum likelihood estimator are discussed. Finite sample properties of the estimator are investigated in an extensive simulation study. An empirical application with the bi-variate series of US stocks and bond returns illustrates the benefits of the new specification with respect to the standard hidden Markov model.","PeriodicalId":320844,"journal":{"name":"PSN: Econometrics","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129600738","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}
This paper introduces a unified framework of counterfactual estimation for time-series cross-sectional data, which estimates the average treatment effect on the treated by directly imputing treated counterfactuals. Its special cases include several newly developed methods, such as the fixed effects counterfactual estimator, interactive fixed effects counterfactual estimator, and matrix completion estimator. These estimators provide more reliable causal estimates than conventional two-way fixed effects models when the treatment effects are heterogeneous or unobserved time-varying confounders exist. Under this framework, we propose two sets of diagnostic tests, tests for (no) pre-trend and placebo tests, accompanied by visualization tools, to help researchers gauge the validity of the no-time-varying-confounder assumption. We illustrate these methods with two political economy examples and develop an open-source package, fect, in both R and Stata to facilitate implementation.
{"title":"A Practical Guide to Counterfactual Estimators for Causal Inference with Time-Series Cross-Sectional Data","authors":"Licheng Liu, Ye Wang, Yiqing Xu","doi":"10.2139/ssrn.3555463","DOIUrl":"https://doi.org/10.2139/ssrn.3555463","url":null,"abstract":"This paper introduces a unified framework of counterfactual estimation for time-series cross-sectional data, which estimates the average treatment effect on the treated by directly imputing treated counterfactuals. Its special cases include several newly developed methods, such as the fixed effects counterfactual estimator, interactive fixed effects counterfactual estimator, and matrix completion estimator. These estimators provide more reliable causal estimates than conventional two-way fixed effects models when the treatment effects are heterogeneous or unobserved time-varying confounders exist. Under this framework, we propose two sets of diagnostic tests, tests for (no) pre-trend and placebo tests, accompanied by visualization tools, to help researchers gauge the validity of the no-time-varying-confounder assumption. We illustrate these methods with two political economy examples and develop an open-source package, fect, in both R and Stata to facilitate implementation.","PeriodicalId":320844,"journal":{"name":"PSN: Econometrics","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115954303","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}
Abstract In this paper, we conduct simultaneous inference of the non-parametric part of a partially linear model when the non-parametric component is a multivariate unknown function. Based on semi-parametric estimates of the model, we construct a simultaneous confidence region of the multivariate function for simultaneous inference. The developed methodology is applied to perform simultaneous inference for the U.S. gasoline demand where the income and price variables are contaminated by Berkson errors. The empirical results strongly suggest that the linearity of the U . S . gasoline demand is rejected. The results are also used to propose an alternative form for the demand.
{"title":"Simultaneous Inference of the Partially Linear Model with a Multivariate Unknown Function","authors":"Kun-Ho Kim, Shih-Kang Chao, W. Härdle","doi":"10.2139/ssrn.3656321","DOIUrl":"https://doi.org/10.2139/ssrn.3656321","url":null,"abstract":"Abstract In this paper, we conduct simultaneous inference of the non-parametric part of a partially linear model when the non-parametric component is a multivariate unknown function. Based on semi-parametric estimates of the model, we construct a simultaneous confidence region of the multivariate function for simultaneous inference. The developed methodology is applied to perform simultaneous inference for the U.S. gasoline demand where the income and price variables are contaminated by Berkson errors. The empirical results strongly suggest that the linearity of the U . S . gasoline demand is rejected. The results are also used to propose an alternative form for the demand.","PeriodicalId":320844,"journal":{"name":"PSN: Econometrics","volume":"1 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":"127976599","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}
We offer a search-theoretic model of statistical discrimination, in which firms treat identical groups unequally based on their occupational choices. The model admits symmetric equilibria in which the group characteristic is ignored, but also asymmetric equilibria in which a group is statistically discriminated against, even when symmetric equilibria are unique. Moreover, a robust possibility is that symmetric equilibria become unstable when the group characteristic is introduced. Unlike most previous literature, our model can justify affirmative action since it eliminates asymmetric equilibria without distorting incentives.
{"title":"A Search Model of Statistical Discrimination","authors":"Jiadong Gu, P. Norman","doi":"10.2139/ssrn.3575199","DOIUrl":"https://doi.org/10.2139/ssrn.3575199","url":null,"abstract":"We offer a search-theoretic model of statistical discrimination, in which firms treat identical groups unequally based on their occupational choices. The model admits symmetric equilibria in which the group characteristic is ignored, but also asymmetric equilibria in which a group is statistically discriminated against, even when symmetric equilibria are unique. Moreover, a robust possibility is that symmetric equilibria become unstable when the group characteristic is introduced. Unlike most previous literature, our model can justify affirmative action since it eliminates asymmetric equilibria without distorting incentives.","PeriodicalId":320844,"journal":{"name":"PSN: Econometrics","volume":"189 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121322864","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}
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