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ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)最新文献

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Study of Discrete Choice Models and Fuzzy Rule Based Systems in the Prediction of Economic Crisis Periods in USA 美国经济危机时期预测中的离散选择模型和模糊规则系统研究
ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic) Pub Date : 2014-05-01 DOI: 10.25102/FER.2014.01.01
Eleftherios Giovanis
{"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}
引用次数: 2
Применение Прогностических Методов Для Среднесрочного и Долгосрочного Планирования (Applying of Prognostic Methods for Medium-Term and Long-Term Periods) ПрименениеПрогностическихМетодовДляСреднесрочногоиДолгосрочногоПланирования(预后的应用方法中期和长期的时间)
ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic) Pub Date : 2014-04-30 DOI: 10.2139/SSRN.2431214
Yurу Tukanоv
{"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}
引用次数: 0
Macro to Micro: Country Exposures, Firm Fundamentals and Stock Returns 宏观到微观:国家风险敞口，坚定的基本面和股票回报
ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic) Pub Date : 2014-04-24 DOI: 10.2139/ssrn.2017091
Ningzhong Li, Scott Richardson, A. I. Tuna
{"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}
引用次数: 88
Is China on Track to Comply with Its 2020 Copenhagen Carbon Intensity Commitment? 中国是否有望履行其2020年哥本哈根碳强度承诺?
ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic) Pub Date : 2014-03-29 DOI: 10.2139/ssrn.2346516
Yuan Yang, Junjie Zhang, C. Wang
{"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}
引用次数: 8
A Univariate Time Varying Analysis of Periodic ARMA Processes 周期ARMA过程的单变量时变分析
ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic) Pub Date : 2014-03-18 DOI: 10.2139/ssrn.2411538
M. Karanasos, A. Paraskevopoulos, Stavros Dafnos
{"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}
引用次数: 5
The Regulation and Value of Prediction Markets 预测市场的规制与价值
ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic) Pub Date : 2014-03-12 DOI: 10.2139/ssrn.3211624
A. Ozimek
{"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}
引用次数: 2
Forecasting Realized Volatility with Changes of Regimes 随制度变化预测已实现波动率
ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic) Pub Date : 2014-02-04 DOI: 10.2139/ssrn.2390780
G. Gallo, E. Otranto
{"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}
引用次数: 3
Asian Development Outlook Forecast Skill 亚洲发展展望预测技能
ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic) Pub Date : 2014-02-01 DOI: 10.2139/ssrn.2479212
Benno Ferrarini
{"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}
引用次数: 2
Machine Learning and Forecast Combination in Incomplete Panels 不完整面板中的机器学习和预测组合
ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic) Pub Date : 2013-12-02 DOI: 10.2139/SSRN.2359523
K. Lahiri, Huaming Peng, Yongchen Zhao
{"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}
引用次数: 10
Building Blocks of Linear Time Series Modeling 线性时间序列建模的构建模块
ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic) Pub Date : 2013-09-16 DOI: 10.2139/ssrn.2326346
Paul I. Louangrath
{"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}
引用次数: 0
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