Hidden Markov Models (HMMs) have emerged as an empirical “workhorse” in the marketing literature in capturing and forecasting within-customer non-stationary behaviors. Extant research has demonstrated that HMMs typically outperform nested benchmarks when examining fit statistics aggregated over individuals and time, but have remained largely silent on the set of dynamic out-of-sample forecasting paths offered by an HMM at the individual level. We examine the capabilities of a two-state HMM using theory and reveal a surprising result: an HMM’s forecasting paths are generally limited to monotonic mean-reverting trajectories. Specifically, they lack the notable flexibility associated with the in-sample state-switching imputations, which are generally (but, as we show, erroneously) presumed to exist in the holdout sample as well. Further, we find that common HMM extensions such as adding more hidden states, allowing for heterogeneity, allowing for covariates, and using hidden semi-Markov models do not alleviate the limited forecasting flexibility. Using a simulation design, we show how these limitations can affect forecasting performance empirically. We discuss implications of the limited forecasting properties of HMMs for researchers and managers.
{"title":"Limited Dynamic Forecasting of Hidden Markov Models","authors":"A. Gopalakrishnan, Eric T. Bradlow, P. Fader","doi":"10.2139/ssrn.3206425","DOIUrl":"https://doi.org/10.2139/ssrn.3206425","url":null,"abstract":"Hidden Markov Models (HMMs) have emerged as an empirical “workhorse” in the marketing literature in capturing and forecasting within-customer non-stationary behaviors. Extant research has demonstrated that HMMs typically outperform nested benchmarks when examining fit statistics aggregated over individuals and time, but have remained largely silent on the set of dynamic out-of-sample forecasting paths offered by an HMM at the individual level. We examine the capabilities of a two-state HMM using theory and reveal a surprising result: an HMM’s forecasting paths are generally limited to monotonic mean-reverting trajectories. Specifically, they lack the notable flexibility associated with the in-sample state-switching imputations, which are generally (but, as we show, erroneously) presumed to exist in the holdout sample as well. Further, we find that common HMM extensions such as adding more hidden states, allowing for heterogeneity, allowing for covariates, and using hidden semi-Markov models do not alleviate the limited forecasting flexibility. Using a simulation design, we show how these limitations can affect forecasting performance empirically. We discuss implications of the limited forecasting properties of HMMs for researchers and managers.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128190845","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 this paper, I outline possibilities and limitations for the scope and performance of predictive analytics within a simple paradigm. I do this by first bifurcating predictive analytics into two categories, passive and active. I contrast this categorization with current alternatives and highlight its relative merits in terms of clarity in boundaries, as well as appropriate methods for different types of prediction. I then describe the range of suitable applications, as well as the possibilities and limitations with regard to prediction accuracy, for each type of prediction. I conclude with a discussion of key ways in which an understanding of this paradigm can be valuable.
{"title":"A Paradigm for Assessing the Scope and Performance of Predictive Analytics","authors":"Jeffrey T. Prince","doi":"10.2139/ssrn.3199961","DOIUrl":"https://doi.org/10.2139/ssrn.3199961","url":null,"abstract":"In this paper, I outline possibilities and limitations for the scope and performance of predictive analytics within a simple paradigm. I do this by first bifurcating predictive analytics into two categories, passive and active. I contrast this categorization with current alternatives and highlight its relative merits in terms of clarity in boundaries, as well as appropriate methods for different types of prediction. I then describe the range of suitable applications, as well as the possibilities and limitations with regard to prediction accuracy, for each type of prediction. I conclude with a discussion of key ways in which an understanding of this paradigm can be valuable.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128177714","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 Over the last few years, Bitcoin and other cryptocurrencies have attracted the interest of many investors, practitioners and researchers. However, little attention has been paid to the predictability of their risk measures. This paper compares the predictability of the one-step-ahead volatility and Value-at-Risk of Bitcoin using several volatility models. We also include procedures that take into account the presence of outliers and estimate the volatility and Value-at-Risk in a robust fashion. Our results show that robust procedures outperform non-robust ones when forecasting the volatility and estimating the Value-at-Risk. These results suggest that the presence of outliers plays an important role in the modelling and forecasting of Bitcoin risk measures.
{"title":"Forecasting Bitcoin Risk Measures: A Robust Approach","authors":"Carlos Trucíos","doi":"10.2139/ssrn.3189446","DOIUrl":"https://doi.org/10.2139/ssrn.3189446","url":null,"abstract":"Abstract Over the last few years, Bitcoin and other cryptocurrencies have attracted the interest of many investors, practitioners and researchers. However, little attention has been paid to the predictability of their risk measures. This paper compares the predictability of the one-step-ahead volatility and Value-at-Risk of Bitcoin using several volatility models. We also include procedures that take into account the presence of outliers and estimate the volatility and Value-at-Risk in a robust fashion. Our results show that robust procedures outperform non-robust ones when forecasting the volatility and estimating the Value-at-Risk. These results suggest that the presence of outliers plays an important role in the modelling and forecasting of Bitcoin risk measures.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132192278","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 study of dependence between random variables is the core of theoretical and applied statistics. Static and dynamic copula models are useful for describing the dependence structure, which is fully encrypted in the copula probability density function. However, these models are not always able to describe the temporal change of the dependence patterns, which is a key characteristic of financial data. We propose a novel nonparametric framework for modelling a time series of copula probability density functions, which allows to forecast the entire function without the need of post-processing procedures to grant positiveness and unit integral. We exploit a suitable isometry that allows to transfer the analysis in a subset of the space of square integrable functions, where we build on nonparametric functional data analysis techniques to perform the analysis. The framework does not assume the densities to belong to any parametric family and it can be successfully applied also to general multivariate probability density functions with bounded or unbounded support. Finally, a noteworthy field of application pertains the study of time varying networks represented through vine copula models. We apply the proposed methodology for estimating and forecasting the time varying dependence structure between the S&P500 and NASDAQ indices.
{"title":"Nonparametric Forecasting of Multivariate Probability Density Functions","authors":"D. Guégan, Matteo Iacopini","doi":"10.2139/ssrn.3192342","DOIUrl":"https://doi.org/10.2139/ssrn.3192342","url":null,"abstract":"The study of dependence between random variables is the core of theoretical and applied statistics. Static and dynamic copula models are useful for describing the dependence structure, which is fully encrypted in the copula probability density function. However, these models are not always able to describe the temporal change of the dependence patterns, which is a key characteristic of financial data. We propose a novel nonparametric framework for modelling a time series of copula probability density functions, which allows to forecast the entire function without the need of post-processing procedures to grant positiveness and unit integral. We exploit a suitable isometry that allows to transfer the analysis in a subset of the space of square integrable functions, where we build on nonparametric functional data analysis techniques to perform the analysis. The framework does not assume the densities to belong to any parametric family and it can be successfully applied also to general multivariate probability density functions with bounded or unbounded support. Finally, a noteworthy field of application pertains the study of time varying networks represented through vine copula models. We apply the proposed methodology for estimating and forecasting the time varying dependence structure between the S&P500 and NASDAQ indices.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"338 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134228711","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 develop a vector autoregressive model with time variation in the mean and the variance. The unobserved time-varying mean is assumed to follow a random walk and we also link it to long-term Consensus forecasts, similar in spirit to so called democratic priors. The changes in variance are modelled via stochastic volatility. The proposed Gibbs sampler allows the researcher to use a large cross-sectional dimension in a feasible amount of computational time. The slowly changing mean can account for a number of secular developments such as changing inflation expectations, slowing productivity growth or demographics. We show the good forecasting performance of the model relative to popular alternatives, including standard Bayesian VARs with Minnesota priors, VARs with democratic priors and standard time-varying parameter VARs for the euro area, the United States and Japan. In particular, incorporating survey forecast information helps to reduce the uncertainty about the unconditional mean and along with the time variation improves the long-run forecasting performance of the VAR models.
{"title":"Forecasting with Bayesian Vector Autoregressions with Time Variation in the Mean","authors":"Marta Bańbura, Andries van Vlodrop","doi":"10.2139/ssrn.3145055","DOIUrl":"https://doi.org/10.2139/ssrn.3145055","url":null,"abstract":"We develop a vector autoregressive model with time variation in the mean and the variance. The unobserved time-varying mean is assumed to follow a random walk and we also link it to long-term Consensus forecasts, similar in spirit to so called democratic priors. The changes in variance are modelled via stochastic volatility. The proposed Gibbs sampler allows the researcher to use a large cross-sectional dimension in a feasible amount of computational time. The slowly changing mean can account for a number of secular developments such as changing inflation expectations, slowing productivity growth or demographics. We show the good forecasting performance of the model relative to popular alternatives, including standard Bayesian VARs with Minnesota priors, VARs with democratic priors and standard time-varying parameter VARs for the euro area, the United States and Japan. In particular, incorporating survey forecast information helps to reduce the uncertainty about the unconditional mean and along with the time variation improves the long-run forecasting performance of the VAR models.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122000110","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}
Michael D Cai, Marco Del Negro, M. Giannoni, Abhijit Sen Gupta, Pearl Li, Erica Moszkowski
The years following the Great Recession were challenging for forecasters. Unlike other deep downturns, this recession was not followed by a swift recovery, but instead generated a sizable and persistent output gap that was not accompanied by deflation as a traditional Phillips curve relationship would have predicted. Moreover, the zero lower bound and unconventional monetary policy generated an unprecedented policy environment. We document the actual real-time forecasting performance of the New York Fed dynamic stochastic general equilibrium (DSGE) model during this period and explain the results using the pseudo real-time forecasting performance results from a battery of DSGE models. We find the New York Fed DSGE model’s forecasting accuracy to be comparable to that of private forecasters, and notably better for output growth than the median forecasts from the FOMC’s Summary of Economic Projections. The model’s financial frictions were key in obtaining these results, as they implied a slow recovery following the financial crisis.
{"title":"DSGE Forecasts of the Lost Recovery","authors":"Michael D Cai, Marco Del Negro, M. Giannoni, Abhijit Sen Gupta, Pearl Li, Erica Moszkowski","doi":"10.2139/ssrn.3138589","DOIUrl":"https://doi.org/10.2139/ssrn.3138589","url":null,"abstract":"The years following the Great Recession were challenging for forecasters. Unlike other deep downturns, this recession was not followed by a swift recovery, but instead generated a sizable and persistent output gap that was not accompanied by deflation as a traditional Phillips curve relationship would have predicted. Moreover, the zero lower bound and unconventional monetary policy generated an unprecedented policy environment. We document the actual real-time forecasting performance of the New York Fed dynamic stochastic general equilibrium (DSGE) model during this period and explain the results using the pseudo real-time forecasting performance results from a battery of DSGE models. We find the New York Fed DSGE model’s forecasting accuracy to be comparable to that of private forecasters, and notably better for output growth than the median forecasts from the FOMC’s Summary of Economic Projections. The model’s financial frictions were key in obtaining these results, as they implied a slow recovery following the financial crisis.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116711713","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 aim of this project is to forecast futures spreads of WTI Crude Oil. The motivation for this project springs from the fact that trading with calendar futures spreads is much more advantageous than trading with many other financial instruments. We make use of the fact that futures prices follow the mean-reverting process (Ornstein-Uhlenbeck process, OU). We develop a method, which was first proposed by Islyaev (2014) and the approach then extended by Sadik et al. (2020), that combines three linear Gaussian state space models, namely one factor model, one factor model with risk premium, and one factor model with seasonality. Thereafter, we directly model futures spreads. Kalman filter and the Maximum Likelihood Estimate (MLE) are used to estimate the model parameters. It is shown that this new approach, using the ratio between the nearest prices over spot prices as a latent variable and calendar futures spreads vector as the observed variable, is more accurate and robust than the indirect forecasting method which inputs both spot prices and futures prices as the latent variable and the observed variable respectively. Results on calibration and comparison for three models and two methods, as well as out-of-sample forecasting results are then presented and discussed.
{"title":"Forecasting Calendar Futures Spreads of Crude Oil Using Kalman Filter","authors":"Xu Ren, G. Mitra, Zryan A Sadik","doi":"10.2139/ssrn.3405998","DOIUrl":"https://doi.org/10.2139/ssrn.3405998","url":null,"abstract":"The aim of this project is to forecast futures spreads of WTI Crude Oil. The motivation for this project springs from the fact that trading with calendar futures spreads is much more advantageous than trading with many other financial instruments. We make use of the fact that futures prices follow the mean-reverting process (Ornstein-Uhlenbeck process, OU). We develop a method, which was first proposed by Islyaev (2014) and the approach then extended by Sadik et al. (2020), that combines three linear Gaussian state space models, namely one factor model, one factor model with risk premium, and one factor model with seasonality. Thereafter, we directly model futures spreads. Kalman filter and the Maximum Likelihood Estimate (MLE) are used to estimate the model parameters. It is shown that this new approach, using the ratio between the nearest prices over spot prices as a latent variable and calendar futures spreads vector as the observed variable, is more accurate and robust than the indirect forecasting method which inputs both spot prices and futures prices as the latent variable and the observed variable respectively. Results on calibration and comparison for three models and two methods, as well as out-of-sample forecasting results are then presented and discussed.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133609666","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 this paper we study what professional forecasters predict. We use spectral analysis and state space modeling to decompose economic time series into a trend, business-cycle, and irregular component. To examine which components are captured by professional forecasters, we regress their forecasts on the estimated components extracted from both the spectral analysis and the state space model. For both decomposition methods we find that the Survey of Professional Forecasters in the short run can predict almost all variation in the time series due to the trend and business-cycle, but the forecasts contain little or no significant information about the variation in the irregular component.
{"title":"What Do Professional Forecasters Actually Predict?","authors":"D. Nibbering, R. Paap, Michel van der Wel","doi":"10.2139/ssrn.2640878","DOIUrl":"https://doi.org/10.2139/ssrn.2640878","url":null,"abstract":"In this paper we study what professional forecasters predict. We use spectral analysis and state space modeling to decompose economic time series into a trend, business-cycle, and irregular component. To examine which components are captured by professional forecasters, we regress their forecasts on the estimated components extracted from both the spectral analysis and the state space model. For both decomposition methods we find that the Survey of Professional Forecasters in the short run can predict almost all variation in the time series due to the trend and business-cycle, but the forecasts contain little or no significant information about the variation in the irregular component.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126711276","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 study reviews the contribution in predictive accuracy of a number of geographic and socio-economic factors that are commonly linked to conflict incidence. A logit model is fitted to sub-national data for Africa at grid-cell level covering the years 2000-2009, generating an out-of-sample forecast for the period 2010-2015. Results show that the strongest predictor of future conflict is current conflict incidence in the grid-cell and neighbouring cells. Additionally, the infant mortality rate, which serves as a proxy for socio-economic well-being, shows some prowess in contributing to accurate predictions. This in contrast with factors such as the share of mountainous terrain. Travel time to the nearest city, to proxy for urban-rural differences, is also a strong predictor, but it must be noted that this could be the result of reporting bias in the outcome variable. In general the results highlight that it is difficult to improve accuracy beyond the contribution of conflict dynamics. Finally, the presented results are based on a relatively simple regression model commonly used in the literature and more sophisticated statistical techniques such as machine learning could improve predictions.
{"title":"Predicting Conflict Events in Africa at Subnational Level","authors":"Stijn van Weezel","doi":"10.2139/ssrn.3019940","DOIUrl":"https://doi.org/10.2139/ssrn.3019940","url":null,"abstract":"This study reviews the contribution in predictive accuracy of a number of geographic and socio-economic factors that are commonly linked to conflict incidence. A logit model is fitted to sub-national data for Africa at grid-cell level covering the years 2000-2009, generating an out-of-sample forecast for the period 2010-2015. Results show that the strongest predictor of future conflict is current conflict incidence in the grid-cell and neighbouring cells. Additionally, the infant mortality rate, which serves as a proxy for socio-economic well-being, shows some prowess in contributing to accurate predictions. This in contrast with factors such as the share of mountainous terrain. Travel time to the nearest city, to proxy for urban-rural differences, is also a strong predictor, but it must be noted that this could be the result of reporting bias in the outcome variable. In general the results highlight that it is difficult to improve accuracy beyond the contribution of conflict dynamics. Finally, the presented results are based on a relatively simple regression model commonly used in the literature and more sophisticated statistical techniques such as machine learning could improve predictions.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"354 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115925693","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}
Juan Antolín-Díaz, Ivan Petrella, J. Rubio-Ramirez
Abstract Macroeconomists constructing conditional forecasts often face a choice between taking a stand on the details of a fully-specified structural model or relying on correlations from VARs and remaining silent about underlying causal mechanisms. This paper develops tools for constructing economically meaningful scenarios with structural VARs, and proposes a metric to assess and compare their plausibility. We provide a unified treatment of conditional forecasting and structural scenario analysis, relating them to entropic tilting. A careful treatment of uncertainty makes our methods suitable for density forecasting and risk assessment. Two applications illustrate our methods: assessing interest-rate forward guidance and stress-testing bank profitability.
{"title":"Structural Scenario Analysis with SVARs","authors":"Juan Antolín-Díaz, Ivan Petrella, J. Rubio-Ramirez","doi":"10.2139/ssrn.3669856","DOIUrl":"https://doi.org/10.2139/ssrn.3669856","url":null,"abstract":"Abstract Macroeconomists constructing conditional forecasts often face a choice between taking a stand on the details of a fully-specified structural model or relying on correlations from VARs and remaining silent about underlying causal mechanisms. This paper develops tools for constructing economically meaningful scenarios with structural VARs, and proposes a metric to assess and compare their plausibility. We provide a unified treatment of conditional forecasting and structural scenario analysis, relating them to entropic tilting. A careful treatment of uncertainty makes our methods suitable for density forecasting and risk assessment. Two applications illustrate our methods: assessing interest-rate forward guidance and stress-testing bank profitability.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130734296","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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