ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)最新文献_第4页

Forecasting Football Match Results in National League Competitions Using Score-Driven Time Series Models 使用分数驱动的时间序列模型预测全国联赛的足球比赛结果

ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic) Pub Date : 2017-07-18 DOI: 10.2139/ssrn.3004605

S. J. Koopman, R. Lit

{"title":"Forecasting Football Match Results in National League Competitions Using Score-Driven Time Series Models","authors":"S. J. Koopman, R. Lit","doi":"10.2139/ssrn.3004605","DOIUrl":"https://doi.org/10.2139/ssrn.3004605","url":null,"abstract":"We develop a new dynamic multivariate model for the analysis and forecasting of football match results in national league competitions. The proposed dynamic model is based on the score of the predictive observation mass function for a high-dimensional panel of weekly match results. Our main interest is in forecasting whether the match result is a win, a loss or a draw for each team. The dynamic model for delivering such forecasts can be based on three different dependent variables: the pairwise count of the number of goals, the difference between the numbers of goals, or the category of the match result (win, loss, draw). The different dependent variables require different distributional assumptions. Furthermore, different dynamic model specifications can be considered for generating the forecasts. We investigate empirically which dependent variable and which dynamic model specification yield the best forecasting results. We validate the precision of the resulting forecasts and the success of the forecasts in a betting simulation in an extensive forecasting study for match results from six large European football competitions. Finally, we conclude that the dynamic model for pairwise counts delivers the most precise forecasts while the dynamic model for the difference between counts is most successful for betting, but that both outperform benchmark and other competing models.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132935708","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: 45

Combining Prediction Markets and Forecasting Contests 结合预测市场和预测竞赛

ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic) Pub Date : 2017-04-05 DOI: 10.2139/ssrn.2944675

Alasdair Brown, J. Reade

引用次数: 1

Forecasting High Frequency Intra-Day Electricity Demand Using Temperature 利用温度预测高频日间电力需求

ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic) Pub Date : 2017-03-28 DOI: 10.2139/ssrn.2958829

James McCulloch, Katja Ignatieva

引用次数: 5

Optimal Selection of Expert Forecasts with Integer Programming 基于整数规划的专家预测最优选择

ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic) Pub Date : 2017-01-04 DOI: 10.2139/ssrn.2894083

D. Matsypura, Ryan Thompson, A. Vasnev

{"title":"Optimal Selection of Expert Forecasts with Integer Programming","authors":"D. Matsypura, Ryan Thompson, A. Vasnev","doi":"10.2139/ssrn.2894083","DOIUrl":"https://doi.org/10.2139/ssrn.2894083","url":null,"abstract":"Combinations of point forecasts from expert forecasters are known to frequently outperform individual forecasts. It is also well documented that combination by simple averaging very often has performance superior to that of more sophisticated combinations. This empirical fact is referred to as the ‘forecast combination puzzle’ in the literature. In this paper, we propose a combination method that exploits this puzzle. Rather than averaging over all forecasts, our method optimally selects forecasts for averaging. The problem of optimal selection is solved using integer programming, a solution approach that has witnessed astonishing advancements. We apply this new method to forecasts of real GDP growth and unemployment from the European Central Bank Survey of Professional Forecasters. The results show that it is optimal to select only a small number of the available forecasts and that averaging over these small subsets almost always provides performance that is superior to averaging over all forecasts. Importantly, this new method is consistently one of the best performers when evaluated against a wide range of alternative forecast combination methods.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134424599","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: 16

Forecasting with Dynamic Panel Data Models 动态面板数据模型预测

ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic) Pub Date : 2016-12-21 DOI: 10.2139/ssrn.2889000

L. Liu, H. Moon, F. Schorfheide

引用次数: 45

Predicting Which Teams Will Make the NBA Playoffs 预测哪些球队将进入NBA季后赛

ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic) Pub Date : 2016-12-17 DOI: 10.2139/ssrn.2764482

Ryan Elmore

引用次数: 1

Methods for Pastcasting, Nowcasting and Forecasting Using Factor-MIDAS: With an Application to Korean GDP 因子- midas的过去预测、临近预测与预测方法——以韩国GDP为例

ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic) Pub Date : 2016-12-06 DOI: 10.2139/ssrn.2998263

Hyun Hak Kim, Norman R. Swanson

{"title":"Methods for Pastcasting, Nowcasting and Forecasting Using Factor-MIDAS: With an Application to Korean GDP","authors":"Hyun Hak Kim, Norman R. Swanson","doi":"10.2139/ssrn.2998263","DOIUrl":"https://doi.org/10.2139/ssrn.2998263","url":null,"abstract":"We utilize mixed frequency factor-MIDAS models for the purpose of carrying out pastcasting, nowcasting, and forecasting experiments using real-time data. We also introduce a new real-time Korean GDP dataset, which is the focus of our experiments. The methodology that we utilize involves first estimating common latent factors (i.e., diffusion indices) from 190 monthly macroeconomic and financial series using various estimation strategies. These factors are then included, along with standard variables measured at multiple different frequencies, in various factor-MIDAS prediction models. Our key empirical findings are that: (i) When using real-time data, factor-MIDAS prediction models outperform various linear benchmark models. Interestingly, the MSFE-best MIDAS models contain no AR lag terms when pastcasting and nowcasting. AR terms only begin to play a role in true forecasting contexts. (ii) Models that utilize only 1 or 2 factors are MSFE-best at all forecasting horizons, but not at any pastcasting and nowcasting horizons. In these latter contexts, much more heavily parameterized models with many factors are preferred. (iii) Real-time data are crucial for forecasting Korean GDP, and the use of first available versus most recent data strongly affects model selection and performance. (iv) Recursively estimated models are almost always MSFE-best, and models estimated using autoregressive interpolation dominate those estimated using other interpolation methods. (v) Factors estimated using recursive principal component estimation methods have more predictive content than those estimated using a variety of other (more sophisticated) approaches. This result is particularly prevalent for our MSFE-best factor-MIDAS models, across virtually all forecast horizons, estimation schemes, and data vintages that are analyzed.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129118609","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

Progress in Economics 经济学进展

ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic) Pub Date : 2016-08-27 DOI: 10.2139/ssrn.2830873

Bradford Tuckfield

引用次数: 0

Inverse Moment Methods for Sufficient Forecasting Using High-Dimensional Predictors 利用高维预测器进行充分预测的逆矩方法

ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic) Pub Date : 2016-08-20 DOI: 10.2139/ssrn.2961010

Wei Luo, Lingzhou Xue, Jiawei Yao

{"title":"Inverse Moment Methods for Sufficient Forecasting Using High-Dimensional Predictors","authors":"Wei Luo, Lingzhou Xue, Jiawei Yao","doi":"10.2139/ssrn.2961010","DOIUrl":"https://doi.org/10.2139/ssrn.2961010","url":null,"abstract":"\u0000 We consider forecasting a single time series using a large number of predictors in the presence of a possible nonlinear forecast function. Assuming that the predictors affect the response through the latent factors, we propose to first conduct factor analysis and then apply sufficient dimension reduction on the estimated factors, to derive the reduced data for subsequent forecasting. Using directional regression and the inverse third-moment method in the stage of sufficient dimension reduction, the proposed methods can capture the non-monotone effect of factors on the response. We also allow a diverging number of factors and only impose general regularity conditions on the distribution of factors, avoiding the undesired time reversibility of the factors by the latter. These make the proposed methods fundamentally more applicable than the sufficient forecasting method in Fan et al. (2017). The proposed methods are demonstrated in both simulation studies and an empirical study of forecasting monthly macroeconomic data from 1959 to 2016. Also, our theory contributes to the literature of sufficient dimension reduction, as it includes an invariance result, a path to perform sufficient dimension reduction under the high-dimensional setting without assuming sparsity, and the corresponding order-determination procedure.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131173214","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 Predictive System for International Trade Growth 国际贸易增长的预测系统

ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic) Pub Date : 2016-08-10 DOI: 10.2139/ssrn.2830290

Sora Chon

{"title":"A Predictive System for International Trade Growth","authors":"Sora Chon","doi":"10.2139/ssrn.2830290","DOIUrl":"https://doi.org/10.2139/ssrn.2830290","url":null,"abstract":"The objective of this paper is to suggest a new predictive system for international trade, based on an unobserved component model. We employ the predictive system developed by Pastor and Stambaugh (2009), which is unlike other conventional predictive regression models. This paper derives an equivalent linear predictive regression from the predictive system, and explains why the proposed predictive system is able to achieve superior out-of-sample predictive power. When predictors are imperfect in an estimated equation, the equation fails to utilize all information from the predictors' past history, and unexplained variations are captured by residuals in the estimated equation. With the use of the predictive system, we can more effectively deal with the dynamics of imperfect predictors. For empirical illustration, we show that, in the case of Korea's export and import growth rates, the predictive system has better out-of-sample predictive powers than the conventional regressions based on Root Mean Squares Error (RMSE). Results from an out-of-sample analysis show that, compared to the benchmark model, the predictive system improves forecast precision by 18.90% for the export growth rate, and by 7.95% for the import growth rate.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128675525","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