This paper studies the economic recessions and the financial crisis in US economy, as these crisis periods affect not only USA but the rest of the world. The wrong government policies and the regulations in bond market among others lead to the longest and deepest financial crisis since the Great depression of 1929. In this paper we examine three models in order to predict the economic recession or expansion periods in USA. The first one is the Logit model, the second is the Probit model and the last one is a fuzzy rule based system binary regression with sigmoid membership function. We examine the in-sample period 1913-2005 and we test the models in the out-of sample period 2006-2009. The estimation results indicate that the fuzzy regression outperforms the Logit and Probit models, especially in the out-of sample period. This indicates that fuzzy regressions provide a better and more reliable signal on whether or not a financial crisis will take place. Furthermore, based on the estimated values for the period 1913-2009 we estimate the forecasts to investigate if the economic recession will be continued or not during 2010. The conclusion is that Logit model presents a signal that the economic recession will be continued during the whole period 2010, while based on Probit and fuzzy regressions the economic recovery might begin in the second half of 2010.
{"title":"Study of Discrete Choice Models and Fuzzy Rule Based Systems in the Prediction of Economic Crisis Periods in USA","authors":"Eleftherios Giovanis","doi":"10.25102/FER.2014.01.01","DOIUrl":"https://doi.org/10.25102/FER.2014.01.01","url":null,"abstract":"This paper studies the economic recessions and the financial crisis in US economy, as these crisis periods affect not only USA but the rest of the world. The wrong government policies and the regulations in bond market among others lead to the longest and deepest financial crisis since the Great depression of 1929. In this paper we examine three models in order to predict the economic recession or expansion periods in USA. The first one is the Logit model, the second is the Probit model and the last one is a fuzzy rule based system binary regression with sigmoid membership function. We examine the in-sample period 1913-2005 and we test the models in the out-of sample period 2006-2009. The estimation results indicate that the fuzzy regression outperforms the Logit and Probit models, especially in the out-of sample period. This indicates that fuzzy regressions provide a better and more reliable signal on whether or not a financial crisis will take place. Furthermore, based on the estimated values for the period 1913-2009 we estimate the forecasts to investigate if the economic recession will be continued or not during 2010. The conclusion is that Logit model presents a signal that the economic recession will be continued during the whole period 2010, while based on Probit and fuzzy regressions the economic recovery might begin in the second half of 2010.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115077027","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}
Russian Abstract: Долгосрочный прогноз макроэкономических и региональных (на уровне субъектов Российской Федерации) показателей является важнейшей компонентой исходной информационной базы для разработки жилищной политики Российской Федерации, в том числе федеральной целевой программы. Цели жилищной политики РФ связаны с формированием рынка доступного жилья эконом-класса, отвечающего требованиям энергоэффективности и экологичности, повышение доступности жилья для жителей субъектов Российской Федерации. Целью данной работы является разработка Прогноза ключевых макроэкономических показателей, определяющих развитие жилищного рынка и жилищного строительства в субъектах Российской Федерации в среднесрочной и долгосрочной перспективе. English Abstract: Long-term forecast of macroeconomic and regional (at the level of subjects of the Russian Federation) performance is a critical component of the source of the information base for the development of the housing policy of the Russian Federation, including the Federal target program. The goal of housing policy of the Russian Federation connected with formation of the affordable housing market economy-class that meets the requirements of energy efficiency and environmental, increase the affordability of housing for inhabitants of subjects of the Russian Federation. The aim of this work is to develop a forecast of key macroeconomic indicators for the housing market and housing construction in regions of the Russian Federation in the medium and long term.
{"title":"Применение Прогностических Методов Для Среднесрочного и Долгосрочного Планирования (Applying of Prognostic Methods for Medium-Term and Long-Term Periods)","authors":"Yurу Tukanоv","doi":"10.2139/SSRN.2431214","DOIUrl":"https://doi.org/10.2139/SSRN.2431214","url":null,"abstract":"Russian Abstract: Долгосрочный прогноз макроэкономических и региональных (на уровне субъектов Российской Федерации) показателей является важнейшей компонентой исходной информационной базы для разработки жилищной политики Российской Федерации, в том числе федеральной целевой программы. Цели жилищной политики РФ связаны с формированием рынка доступного жилья эконом-класса, отвечающего требованиям энергоэффективности и экологичности, повышение доступности жилья для жителей субъектов Российской Федерации. Целью данной работы является разработка Прогноза ключевых макроэкономических показателей, определяющих развитие жилищного рынка и жилищного строительства в субъектах Российской Федерации в среднесрочной и долгосрочной перспективе. English Abstract: Long-term forecast of macroeconomic and regional (at the level of subjects of the Russian Federation) performance is a critical component of the source of the information base for the development of the housing policy of the Russian Federation, including the Federal target program. The goal of housing policy of the Russian Federation connected with formation of the affordable housing market economy-class that meets the requirements of energy efficiency and environmental, increase the affordability of housing for inhabitants of subjects of the Russian Federation. The aim of this work is to develop a forecast of key macroeconomic indicators for the housing market and housing construction in regions of the Russian Federation in the medium and long term.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114505125","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 outline a systematic approach to incorporate macroeconomic information into firm level forecasting from the perspective of an equity investor. Using a global sample of 198,315 firm-years over the 1998–2010 time period, we find that combining firm level exposures to countries (via geographic segment data) with forecasts of country level performance, is able to generate superior forecasts for firm fundamentals. This result is particularly evident for purely domestic firms. We further find that this forecasting benefit is associated with future excess stock returns. These relations are stronger after periods of higher dispersion in expected country level performance.
{"title":"Macro to Micro: Country Exposures, Firm Fundamentals and Stock Returns","authors":"Ningzhong Li, Scott Richardson, A. I. Tuna","doi":"10.2139/ssrn.2017091","DOIUrl":"https://doi.org/10.2139/ssrn.2017091","url":null,"abstract":"We outline a systematic approach to incorporate macroeconomic information into firm level forecasting from the perspective of an equity investor. Using a global sample of 198,315 firm-years over the 1998–2010 time period, we find that combining firm level exposures to countries (via geographic segment data) with forecasts of country level performance, is able to generate superior forecasts for firm fundamentals. This result is particularly evident for purely domestic firms. We further find that this forecasting benefit is associated with future excess stock returns. These relations are stronger after periods of higher dispersion in expected country level performance.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124905959","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}
In the 2009 Copenhagen Accord, China agreed to slash its carbon intensity (carbon dioxide emissions/GDP) by 40% to 45% from the 2005 level by 2020. We assess whether China can achieve the target under the business-as-usual scenario by forecasting its emissions from energy consumption. Our preferred model shows that China's carbon intensity is projected to decline by only 33%. The results imply that China needs additional mitigation effort to comply with the Copenhagen commitment. In addition, China's baseline emissions are projected to increase by 56% in the next decade (2011-2020). The emission growth is more than triple the emission reductions that the European Union and the United States have committed to in the same period.
{"title":"Is China on Track to Comply with Its 2020 Copenhagen Carbon Intensity Commitment?","authors":"Yuan Yang, Junjie Zhang, C. Wang","doi":"10.2139/ssrn.2346516","DOIUrl":"https://doi.org/10.2139/ssrn.2346516","url":null,"abstract":"In the 2009 Copenhagen Accord, China agreed to slash its carbon intensity (carbon dioxide emissions/GDP) by 40% to 45% from the 2005 level by 2020. We assess whether China can achieve the target under the business-as-usual scenario by forecasting its emissions from energy consumption. Our preferred model shows that China's carbon intensity is projected to decline by only 33%. The results imply that China needs additional mitigation effort to comply with the Copenhagen commitment. In addition, China's baseline emissions are projected to increase by 56% in the next decade (2011-2020). The emission growth is more than triple the emission reductions that the European Union and the United States have committed to in the same period.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127792127","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 standard approach for studying the periodic ARMA model with coefficients that vary over the seasons is to express it in a vector form. In this paper we introduce an alternative method which views the periodic formulation as a time varying univariate process and obviates the need for vector analysis. The specification, interpretation, and solution of a periodic ARMA process enable us to formulate a forecasting method which avoids recursion and allows us to obtain analytic expressions of the optimal predictors. Our results on periodic models are general, analogous to those for stationary specifications, and place the former on the same computational basis as the latter.
{"title":"A Univariate Time Varying Analysis of Periodic ARMA Processes","authors":"M. Karanasos, A. Paraskevopoulos, Stavros Dafnos","doi":"10.2139/ssrn.2411538","DOIUrl":"https://doi.org/10.2139/ssrn.2411538","url":null,"abstract":"The standard approach for studying the periodic ARMA model with coefficients that vary over the seasons is to express it in a vector form. In this paper we introduce an alternative method which views the periodic formulation as a time varying univariate process and obviates the need for vector analysis. The specification, interpretation, and solution of a periodic ARMA process enable us to formulate a forecasting method which avoids recursion and allows us to obtain analytic expressions of the optimal predictors. Our results on periodic models are general, analogous to those for stationary specifications, and place the former on the same computational basis as the latter.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131997250","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}
Prediction markets are important information-aggregation tools for researchers, businesses, individuals, and governments. This paper provides an overview of why prediction markets matter, how they are regulated, and how the regulation can be improved. The value of prediction markets is illustrated with discussions of their forecasting ability and the characteristics thesemarkets possess which give them advantages over other means of forecasting and information aggregation. The past, current, and future regulatory environment is surveyed.
{"title":"The Regulation and Value of Prediction Markets","authors":"A. Ozimek","doi":"10.2139/ssrn.3211624","DOIUrl":"https://doi.org/10.2139/ssrn.3211624","url":null,"abstract":"Prediction markets are important information-aggregation tools for researchers, businesses, individuals, and governments. This paper provides an overview of why prediction markets matter, how they are regulated, and how the regulation can be improved. The value of prediction markets is illustrated with discussions of their forecasting ability and the characteristics thesemarkets possess which give them advantages over other means of forecasting and information aggregation. The past, current, and future regulatory environment is surveyed.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134467183","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}
Realized volatility of financial time series generally shows a slow–moving average level from the early 2000s to recent times, with alternating periods of turmoil and quiet. Modeling such a pattern has been variously tackled in the literature with solutions spanning from long–memory, Markov switching and spline interpolation. In this paper, we explore the extension of Multiplicative Error Models to include a Markovian dynamics (MS-MEM). Such a model is able to capture some sudden changes in volatility following an abrupt crisis and to accommodate different dynamic responses within each regime. The model is applied to the realized volatility of the S&P500 index: next to an interesting interpretation of the regimes in terms of market events, the MS-MEM has better in–sample fitting capability and achieves good out–of–sample forecasting performances relative to alternative specifications.
{"title":"Forecasting Realized Volatility with Changes of Regimes","authors":"G. Gallo, E. Otranto","doi":"10.2139/ssrn.2390780","DOIUrl":"https://doi.org/10.2139/ssrn.2390780","url":null,"abstract":"Realized volatility of financial time series generally shows a slow–moving average level from the early 2000s to recent times, with alternating periods of turmoil and quiet. Modeling such a pattern has been variously tackled in the literature with solutions spanning from long–memory, Markov switching and spline interpolation. In this paper, we explore the extension of Multiplicative Error Models to include a Markovian dynamics (MS-MEM). Such a model is able to capture some sudden changes in volatility following an abrupt crisis and to accommodate different dynamic responses within each regime. The model is applied to the realized volatility of the S&P500 index: next to an interesting interpretation of the regimes in terms of market events, the MS-MEM has better in–sample fitting capability and achieves good out–of–sample forecasting performances relative to alternative specifications.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127917908","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 Asian Development Outlook (ADO) provides growth and inflation forecasts for more than 40 economies in the region. This paper assesses the accuracy of those forecasts against actual outcomes for the years from 2008 to 2011. The World Economic Outlook (WEO) forecasts by the International Monetary Fund are used as a benchmark against which to derive a comparative measure of the accuracy of ADO forecasts, or skill. ADO is found to be ‘more skillful’ than WEO in estimating both current-year gross domestic product (GDP) growth and consumer price index (CPI) inflation of Asian economies. WEO may have an edge over ADO when it comes to year-ahead GDP forecasts, while ADO’s inflation forecasts tend to be more accurate. By and large, and notwithstanding much heterogeneity across economies and years, both sets of forecasts display a high degree of inaccuracy during the crisis years.
{"title":"Asian Development Outlook Forecast Skill","authors":"Benno Ferrarini","doi":"10.2139/ssrn.2479212","DOIUrl":"https://doi.org/10.2139/ssrn.2479212","url":null,"abstract":"The Asian Development Outlook (ADO) provides growth and inflation forecasts for more than 40 economies in the region. This paper assesses the accuracy of those forecasts against actual outcomes for the years from 2008 to 2011. The World Economic Outlook (WEO) forecasts by the International Monetary Fund are used as a benchmark against which to derive a comparative measure of the accuracy of ADO forecasts, or skill. ADO is found to be ‘more skillful’ than WEO in estimating both current-year gross domestic product (GDP) growth and consumer price index (CPI) inflation of Asian economies. WEO may have an edge over ADO when it comes to year-ahead GDP forecasts, while ADO’s inflation forecasts tend to be more accurate. By and large, and notwithstanding much heterogeneity across economies and years, both sets of forecasts display a high degree of inaccuracy during the crisis years.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133059510","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 focuses on the newly proposed on-line forecast combination algorithms in Sancetta (2010), Yang (2004), and Wei and Yang (2012). We first establish the asymptotic relationship between these new algorithms and the Bates and Granger (1969) method. Then, we show that when implemented on unbalanced panels, different combination algorithms implicitly impute missing data differently, making results not comparable across methods. Using forecasts of a number of macroeconomic variables from the U.S. Survey of Professional Forecasters, we evaluate the performance of the new algorithms and contrast their inner mechanisms with that of Bates and Granger's method. Missing data in the SPF panels are specifically controlled for by explicit imputation. We find that even though equally weighted average is hard to beat, the new algorithms deliver superior performance especially during periods of volatility clustering and structural breaks.
本文重点研究了Sancetta(2010)、Yang(2004)和Wei and Yang(2012)提出的在线预测组合算法。我们首先建立了这些新算法与Bates and Granger(1969)方法之间的渐近关系。然后，我们表明，当在不平衡面板上实现时，不同的组合算法隐式地以不同的方式输入缺失数据，使得结果无法跨方法进行比较。利用来自美国专业预测者调查的一些宏观经济变量的预测，我们评估了新算法的性能，并将其内部机制与Bates和Granger的方法进行了对比。SPF面板上的缺失数据通过显式插入来特别控制。我们发现，尽管等加权平均很难被击败，但新算法在波动聚类和结构断裂期间提供了卓越的性能。
{"title":"Machine Learning and Forecast Combination in Incomplete Panels","authors":"K. Lahiri, Huaming Peng, Yongchen Zhao","doi":"10.2139/SSRN.2359523","DOIUrl":"https://doi.org/10.2139/SSRN.2359523","url":null,"abstract":"This paper focuses on the newly proposed on-line forecast combination algorithms in Sancetta (2010), Yang (2004), and Wei and Yang (2012). We first establish the asymptotic relationship between these new algorithms and the Bates and Granger (1969) method. Then, we show that when implemented on unbalanced panels, different combination algorithms implicitly impute missing data differently, making results not comparable across methods. Using forecasts of a number of macroeconomic variables from the U.S. Survey of Professional Forecasters, we evaluate the performance of the new algorithms and contrast their inner mechanisms with that of Bates and Granger's method. Missing data in the SPF panels are specifically controlled for by explicit imputation. We find that even though equally weighted average is hard to beat, the new algorithms deliver superior performance especially during periods of volatility clustering and structural breaks.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130434725","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}
Three models are presented: AR (autoregressive), MA (moving average) and ARMA (autoregressive moving average) are common models used in time series forecasting. These three models are the various definition of each element of the General Linear Model: Y = a + b + c. For the study of linear behavior of data, these three models are helpful. However, the limitation of these models starts to surface when nonlinear behavior of data appears. Linear behavior is characterized by a straight line mapping the response variable (Y) to each unit change in the explanatory variable (X). If the research involves human emotion, preferences, or level of tolerance and the data series does not manifest a straight line, AR, MA and ARMA may not be as useful. Price tolerance versus utility, for instance is a good example to illustrate where the general linear model may not be useful. N such cases, a higher order polynomial modeling may be used.
提出了三种模型:AR(自回归)、MA(移动平均)和ARMA(自回归移动平均)是时间序列预测中常用的模型。这三种模型是一般线性模型Y = a + b + c中各元素的各种定义。对于研究数据的线性行为，这三种模型是有帮助的。然而，当数据的非线性行为出现时，这些模型的局限性就开始显现出来。线性行为的特征是反应变量(Y)与解释变量(X)的每个单位变化之间的直线关系。如果研究涉及人类情感、偏好或容忍水平，并且数据序列不是直线，则AR、MA和ARMA可能没有那么有用。例如，价格容忍度与效用的对比就是一个很好的例子，可以说明一般线性模型在哪些地方可能不太有用。在这种情况下，可以使用高阶多项式建模。
{"title":"Building Blocks of Linear Time Series Modeling","authors":"Paul I. Louangrath","doi":"10.2139/ssrn.2326346","DOIUrl":"https://doi.org/10.2139/ssrn.2326346","url":null,"abstract":"Three models are presented: AR (autoregressive), MA (moving average) and ARMA (autoregressive moving average) are common models used in time series forecasting. These three models are the various definition of each element of the General Linear Model: Y = a + b + c. For the study of linear behavior of data, these three models are helpful. However, the limitation of these models starts to surface when nonlinear behavior of data appears. Linear behavior is characterized by a straight line mapping the response variable (Y) to each unit change in the explanatory variable (X). If the research involves human emotion, preferences, or level of tolerance and the data series does not manifest a straight line, AR, MA and ARMA may not be as useful. Price tolerance versus utility, for instance is a good example to illustrate where the general linear model may not be useful. N such cases, a higher order polynomial modeling may be used.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124810734","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：