This study examines the relative importance of percentage change in price-to-earnings ratio (PE), percentage change in dividend yield (DY) and change in aggregate Tobin’s q ratio (∆TBQ) in forecasting returns on the S&P 500 (SP). The results from the variance decomposition analysis of quarterly data from 1951Q4 to 2012Q4 show that ∆TBQ beats out PE and DY in forecasting SP. While PE and DY together only forecast about 3.2% of SP at the two-quarter to eight-quarter horizons, ∆TBQ does so at about 66%. The Granger-causality test results reveal that ∆TBQ Granger-causes PE and DY. The generalized impulse response functions of the three variables are also estimated.
{"title":"Stock Market Performance: Variance Decomposition of Price-Earnings Ratio, Dividend Yield and Tobin's Q","authors":"V. Sum","doi":"10.2139/ssrn.2293532","DOIUrl":"https://doi.org/10.2139/ssrn.2293532","url":null,"abstract":"This study examines the relative importance of percentage change in price-to-earnings ratio (PE), percentage change in dividend yield (DY) and change in aggregate Tobin’s q ratio (∆TBQ) in forecasting returns on the S&P 500 (SP). The results from the variance decomposition analysis of quarterly data from 1951Q4 to 2012Q4 show that ∆TBQ beats out PE and DY in forecasting SP. While PE and DY together only forecast about 3.2% of SP at the two-quarter to eight-quarter horizons, ∆TBQ does so at about 66%. The Granger-causality test results reveal that ∆TBQ Granger-causes PE and DY. The generalized impulse response functions of the three variables are also estimated.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"176 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123177069","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}
On the basis of keyword searches in newspaper articles, several versions of the Recession-Word Index (RWI) are constructed for Germany and Switzerland. We use these indices in order to track the business cycle dynamics in these two countries. Our main findings are the following. First, we show that augmenting benchmark auto-regressive models with the RWI generally leads to improvement in accuracy of one-step ahead forecasts of GDP, compared to those obtained by the benchmark model. Second, the accuracy of out-of-sample forecasts obtained with models augmented with the RWI is comparable to that of models augmented with established economic indicators in both countries; such as the Ifo Business Climate Index and the ZEW Indicator of Economic Sentiment for Germany, and the KOF Economic Barometer and the Purchasing Managers Index in manufacturing for Switzerland. Third, we show that the RWI-based forecasts are more accurate than the consensus forecasts, (published by Consensus Economics, Inc.), for Switzerland, whereas we reach the opposite conclusion for Germany. In fact, the accuracy of the consensus forecasts of GDP growth for Germany appears to be superior to that of any other indicator considered in our study. These results are robust to changes in estimation/forecast samples, the use of rolling vs.expanding estimation windows, and the inclusion of a web-based recession indicator extracted from Google Trends into a set of the competing models.
{"title":"Using Newspapers for Tracking the Business Cycle: A Comparative Study for Germany and Switzerland","authors":"David Iselin, Boriss Siliverstovs","doi":"10.2139/ssrn.2280245","DOIUrl":"https://doi.org/10.2139/ssrn.2280245","url":null,"abstract":"On the basis of keyword searches in newspaper articles, several versions of the Recession-Word Index (RWI) are constructed for Germany and Switzerland. We use these indices in order to track the business cycle dynamics in these two countries. Our main findings are the following. First, we show that augmenting benchmark auto-regressive models with the RWI generally leads to improvement in accuracy of one-step ahead forecasts of GDP, compared to those obtained by the benchmark model. Second, the accuracy of out-of-sample forecasts obtained with models augmented with the RWI is comparable to that of models augmented with established economic indicators in both countries; such as the Ifo Business Climate Index and the ZEW Indicator of Economic Sentiment for Germany, and the KOF Economic Barometer and the Purchasing Managers Index in manufacturing for Switzerland. Third, we show that the RWI-based forecasts are more accurate than the consensus forecasts, (published by Consensus Economics, Inc.), for Switzerland, whereas we reach the opposite conclusion for Germany. In fact, the accuracy of the consensus forecasts of GDP growth for Germany appears to be superior to that of any other indicator considered in our study. These results are robust to changes in estimation/forecast samples, the use of rolling vs.expanding estimation windows, and the inclusion of a web-based recession indicator extracted from Google Trends into a set of the competing models.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133627336","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}
Pub Date : 2013-05-01DOI: 10.5089/9781484301357.001.A001
Michal Andrle
This paper introduces methods that allow analysts to (i) decompose the estimates of unobserved quantities into observed data, (ii) to better understand revision properties of the model, and (iii) to impose subjective prior constraints on path estimates of unobserved shocks in structural economic models. For instance, a decomposition of the flexible-price output gap, or a technology shock, into contributions of output, inflation, interest rates, and other observed variables' contribution is feasible. The intuitive nature and analytical clarity of the suggested procedures are appealing for policy-related and forecasting models.
{"title":"Understanding DSGE Filters in Forecasting and Policy Analysis","authors":"Michal Andrle","doi":"10.5089/9781484301357.001.A001","DOIUrl":"https://doi.org/10.5089/9781484301357.001.A001","url":null,"abstract":"This paper introduces methods that allow analysts to (i) decompose the estimates of unobserved quantities into observed data, (ii) to better understand revision properties of the model, and (iii) to impose subjective prior constraints on path estimates of unobserved shocks in structural economic models. For instance, a decomposition of the flexible-price output gap, or a technology shock, into contributions of output, inflation, interest rates, and other observed variables' contribution is feasible. The intuitive nature and analytical clarity of the suggested procedures are appealing for policy-related and forecasting models.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127811392","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}
C. Diks, V. Panchenko, Oleg Sokolinskiy, Dick J. C. van Dijk
This paper develops a testing framework for comparing the predictive accuracy of copula-based multivariate density forecasts, focusing on a specific part of the joint distribution. The test is framed in the context of the Kullback-Leibler Information Criterion, but using (out-of-sample) conditional likelihood and censored likelihood in order to focus the evaluation on the region of interest. Monte Carlo simulations document that the resulting test statistics have satisfactory size and power properties in small samples. In an empirical application to daily exchange rate returns we find evidence that the dependence structure varies with the sign and magnitude of returns, such that different parametric copula models achieve superior forecasting performance in different regions of the support. Our analysis highlights the importance of allowing for lower and upper tail dependence for accurate forecasting of common extreme appreciation and depreciation of different currencies.
{"title":"Comparing the Accuracy of Copula-Based Multivariate Density Forecasts in Selected Regions of Support","authors":"C. Diks, V. Panchenko, Oleg Sokolinskiy, Dick J. C. van Dijk","doi":"10.2139/ssrn.2254892","DOIUrl":"https://doi.org/10.2139/ssrn.2254892","url":null,"abstract":"This paper develops a testing framework for comparing the predictive accuracy of copula-based multivariate density forecasts, focusing on a specific part of the joint distribution. The test is framed in the context of the Kullback-Leibler Information Criterion, but using (out-of-sample) conditional likelihood and censored likelihood in order to focus the evaluation on the region of interest. Monte Carlo simulations document that the resulting test statistics have satisfactory size and power properties in small samples. In an empirical application to daily exchange rate returns we find evidence that the dependence structure varies with the sign and magnitude of returns, such that different parametric copula models achieve superior forecasting performance in different regions of the support. Our analysis highlights the importance of allowing for lower and upper tail dependence for accurate forecasting of common extreme appreciation and depreciation of different currencies.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133279078","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 2008, the S&P500 aggregated a loss of 30.16% during three selected days. Unfortunately, benchmark risk measures didn't forecast these hazards. Consequently, we witness a growing interest in coherent risk measures, sensitive to high moments and heavy tail risk. Such measures were proposed by Aumann-Serrano (2007) and Foster-Hart (2008). As a generalization of these measures, we construct the FEER Index, coherent down-side risk measure "Forecasting Extreme Events Risk", sensitive to heavy tail risk. We present closed-form solution as a function of the return moments, V aR and CV aR. Furthermore, the FEER Index is dynamically calibrated to the market, becoming a live seismograph for market catastrophes.
{"title":"FEER Index - Forecasting Extreme Events Risk","authors":"Amitay Kauffmann, Gal Zahavi","doi":"10.2139/ssrn.2070914","DOIUrl":"https://doi.org/10.2139/ssrn.2070914","url":null,"abstract":"In 2008, the S&P500 aggregated a loss of 30.16% during three selected days. Unfortunately, benchmark risk measures didn't forecast these hazards. Consequently, we witness a growing interest in coherent risk measures, sensitive to high moments and heavy tail risk. Such measures were proposed by Aumann-Serrano (2007) and Foster-Hart (2008). As a generalization of these measures, we construct the FEER Index, coherent down-side risk measure \"Forecasting Extreme Events Risk\", sensitive to heavy tail risk. We present closed-form solution as a function of the return moments, V aR and CV aR. Furthermore, the FEER Index is dynamically calibrated to the market, becoming a live seismograph for market catastrophes.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122634373","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 an otherwise unique-equilibrium model, agents are segmented into a few informational islands according to the signal they receive about others' expectations. Even if agents perfectly observe fundamentals, rational-exuberance equilibria (REX) can arise as they put weight on expectational signals to refine their forecasts. Constant-gain adaptive learning can trigger jumps between the equilibrium where only fundamentals are weighted and a REX. This determines regime switching in macro volatility despite unchanged monetary policy and time-invariant distribution of exogenous shocks. In this context, a tight inflation-targeting policy can lower expectational complementarity preventing rational exuberance, although its effect is non-monotone.
{"title":"Good Luck or Good Policy? An Expectational Theory of Macro-Volatility Switches","authors":"Gaetano Gaballo","doi":"10.2139/ssrn.2164292","DOIUrl":"https://doi.org/10.2139/ssrn.2164292","url":null,"abstract":"In an otherwise unique-equilibrium model, agents are segmented into a few informational islands according to the signal they receive about others' expectations. Even if agents perfectly observe fundamentals, rational-exuberance equilibria (REX) can arise as they put weight on expectational signals to refine their forecasts. Constant-gain adaptive learning can trigger jumps between the equilibrium where only fundamentals are weighted and a REX. This determines regime switching in macro volatility despite unchanged monetary policy and time-invariant distribution of exogenous shocks. In this context, a tight inflation-targeting policy can lower expectational complementarity preventing rational exuberance, although its effect is non-monotone.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129217627","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}
Using aggregate quarterly data for the period 1975q1–2010q4, I find that the US housing market changed from a stable regime with prices determined by fundamentals, to a highly unstable regime at the beginning of the previous decade. My results indicate that these imbalances could have been detected with the aid of real time econometric modeling and that they were caused by the sharp rise in subprime lending in the early to mid 2000s. These results are based on the detection of huge parameter non-constancies and a loss of equilibrium correction in two theory derived cointegrating relationships shown to be very stable for earlier periods. Controlling for the increased subprime exposure during this period, enables me to reestablish the pre-break relationships also for the full sample. This suggests that the US housing bubble was caused by the increased borrowing to a more risky segment of the market, which may have allowed for a latent frenzy behavior that previously was constrained by the lack of financing. With reference to Stiglitz’s general conception of a bubble, I use the econometric results to construct two bubble indicators, which clearly demonstrate the transition to an unstable regime. Such indicators can be part of an early warning system and are shown to Granger cause a set of coincident indicators and financial (in)stability measures.
{"title":"Econometric Regime Shifts and the US Subprime Bubble","authors":"A. Anundsen","doi":"10.2139/ssrn.2163517","DOIUrl":"https://doi.org/10.2139/ssrn.2163517","url":null,"abstract":"Using aggregate quarterly data for the period 1975q1–2010q4, I find that the US housing market changed from a stable regime with prices determined by fundamentals, to a highly unstable regime at the beginning of the previous decade. My results indicate that these imbalances could have been detected with the aid of real time econometric modeling and that they were caused by the sharp rise in subprime lending in the early to mid 2000s. These results are based on the detection of huge parameter non-constancies and a loss of equilibrium correction in two theory derived cointegrating relationships shown to be very stable for earlier periods. Controlling for the increased subprime exposure during this period, enables me to reestablish the pre-break relationships also for the full sample. This suggests that the US housing bubble was caused by the increased borrowing to a more risky segment of the market, which may have allowed for a latent frenzy behavior that previously was constrained by the lack of financing. With reference to Stiglitz’s general conception of a bubble, I use the econometric results to construct two bubble indicators, which clearly demonstrate the transition to an unstable regime. Such indicators can be part of an early warning system and are shown to Granger cause a set of coincident indicators and financial (in)stability measures.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128527236","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 news impact curve of EGARCH captures the asymmetric impact of negative news on volatility. It also captures the impact of large shocks, negative and positive. The interpretation of the curve is complicated by its composition, making it difficult to interpret its coefficients and the information in its terms. An attempt is made to simplify the interpretation of the coefficients of the EGARCH in order to capture the information in the specification. An example is used to illustrate the interpretation.
{"title":"Assessing the Impact of News on Volatility Using the News Impact Curve of EGARCH","authors":"Meera Sharma","doi":"10.2139/ssrn.2085244","DOIUrl":"https://doi.org/10.2139/ssrn.2085244","url":null,"abstract":"The news impact curve of EGARCH captures the asymmetric impact of negative news on volatility. It also captures the impact of large shocks, negative and positive. The interpretation of the curve is complicated by its composition, making it difficult to interpret its coefficients and the information in its terms. An attempt is made to simplify the interpretation of the coefficients of the EGARCH in order to capture the information in the specification. An example is used to illustrate the interpretation.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130708990","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 recent financial crisis had a substantial impact on equity and bond markets, as well as on the performances of managed portfolios which have been hit by the decrease of both indices. Nevertheless, the availability of indices monitoring the equity market volatility, the VIX index, credit markets default risk, and CDS indices, allows for the construction of hedging strategies. In this paper, we take the point of view of an equity investor who wants to hedge the equity risk by taking positions either on the VIX index or on CDS indices. In deriving the hedge ratios, we consider the joint dynamic of variables taking into account mean relations, variance spillovers, and asymmetry, as well as correlation changes over time. Our analysis is based on sectorial indices and shows the advantages of hedging and the impact of a model specification.
{"title":"Equity and CDS Sector Indices: Dynamic Models and Risk Hedging","authors":"M. Caporin","doi":"10.2139/ssrn.2071719","DOIUrl":"https://doi.org/10.2139/ssrn.2071719","url":null,"abstract":"The recent financial crisis had a substantial impact on equity and bond markets, as well as on the performances of managed portfolios which have been hit by the decrease of both indices. Nevertheless, the availability of indices monitoring the equity market volatility, the VIX index, credit markets default risk, and CDS indices, allows for the construction of hedging strategies. In this paper, we take the point of view of an equity investor who wants to hedge the equity risk by taking positions either on the VIX index or on CDS indices. In deriving the hedge ratios, we consider the joint dynamic of variables taking into account mean relations, variance spillovers, and asymmetry, as well as correlation changes over time. Our analysis is based on sectorial indices and shows the advantages of hedging and the impact of a model specification.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128716793","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}
K. C. Lichtendahl, Y. Grushka-Cockayne, R. L. Winkler
We consider two ways to aggregate expert opinions using simple averages: averaging probabilities and averaging quantiles. We examine analytical properties of these forecasts and compare their ability to harness the wisdom of the crowd. In terms of location, the two average forecasts have the same mean. The average quantile forecast is always sharper: it has lower variance than the average probability forecast. Even when the average probability forecast is overconfident, the shape of the average quantile forecast still offers the possibility of a better forecast. Using probability forecasts for gross domestic product growth and inflation from the Survey of Professional Forecasters, we present evidence that both when the average probability forecast is overconfident and when it is underconfident, it is outperformed by the average quantile forecast. Our results show that averaging quantiles is a viable alternative and indicate some conditions under which it is likely to be more useful than averaging probabilities. This paper was accepted by Peter Wakker, decision analysis.
我们考虑了两种使用简单平均数来汇总专家意见的方法:平均概率和平均分位数。我们考察了这些预测的分析性质，并比较了它们利用大众智慧的能力。就位置而言，两个平均预报的平均值相同。平均分位数预测总是更清晰:它的方差低于平均概率预测。即使当平均概率预测过于自信时，平均分位数预测的形状仍然提供了更好预测的可能性。利用《专业预测者调查》(Survey of Professional forecasts)对国内生产总值(gdp)增长和通胀的概率预测，我们提出证据表明，无论是在平均概率预测过于自信还是过于自信时，它的表现都优于平均分位数预测。我们的结果表明，平均分位数是一种可行的替代方案，并指出在某些条件下，它可能比平均概率更有用。这篇论文被决策分析的Peter Wakker接受。
{"title":"Is it Better to Average Probabilities or Quantiles?","authors":"K. C. Lichtendahl, Y. Grushka-Cockayne, R. L. Winkler","doi":"10.2139/ssrn.2066806","DOIUrl":"https://doi.org/10.2139/ssrn.2066806","url":null,"abstract":"We consider two ways to aggregate expert opinions using simple averages: averaging probabilities and averaging quantiles. We examine analytical properties of these forecasts and compare their ability to harness the wisdom of the crowd. In terms of location, the two average forecasts have the same mean. The average quantile forecast is always sharper: it has lower variance than the average probability forecast. Even when the average probability forecast is overconfident, the shape of the average quantile forecast still offers the possibility of a better forecast. Using probability forecasts for gross domestic product growth and inflation from the Survey of Professional Forecasters, we present evidence that both when the average probability forecast is overconfident and when it is underconfident, it is outperformed by the average quantile forecast. Our results show that averaging quantiles is a viable alternative and indicate some conditions under which it is likely to be more useful than averaging probabilities. This paper was accepted by Peter Wakker, decision analysis.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116721714","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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