ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)最新文献_第7页

The Importance of Updating: Evidence from a Brazilian Nowcasting Model 更新的重要性:来自巴西临近预报模型的证据

ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic) Pub Date : 2014-11-01 DOI: 10.2139/ssrn.2529168

D. Bragoli, Luca Metelli, M. Modugno

{"title":"The Importance of Updating: Evidence from a Brazilian Nowcasting Model","authors":"D. Bragoli, Luca Metelli, M. Modugno","doi":"10.2139/ssrn.2529168","DOIUrl":"https://doi.org/10.2139/ssrn.2529168","url":null,"abstract":"How often should we update predictions for economic activity? Gross domestic product is a quarterly variable disseminated usually a couple of months after the end of the quarter, but many other macroeconomic indicators are released with a higher frequency, and financial markets react very strongly to them. However, most of the professional forecasters, including the IMF, the OECD, and most central banks, tend to update their forecasts of economic activity only two to four times a year. The Central Bank of Brazil, not only disseminates its official forecasts every quarter as other central banks, but also collects and publishes the results of professional forecasters’ survey data at a daily frequency. The aim of this article is to evaluate the forecasting performance of the Central Bank of Brazil Survey and to compare it with the mechanical forecasts based on state-of-the-art nowcasting techniques. Results indicate that both model and market participant predictions are well behaved, i.e. as more information becomes available their accuracy and correlation with the actual realization increases. In terms of performance the model seems to be slightly better than the institutional forecasts in the nowcast and backcast. Keywords: Nowcasting, Updating, Dynamic Factor Model. JEL classification: C33, C53, E37.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131164534","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: 41

Using Naïve Forecasts to Assess Limits to Forecast Accuracy and the Quality of Fit of Forecasts to Time Series Data 使用Naïve预测来评估预测精度的限制和预测对时间序列数据的拟合质量

ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic) Pub Date : 2014-10-26 DOI: 10.2139/ssrn.2515072

P. Goodwin

引用次数: 4

Nowcasting Global Economic Growth: A Factor-Augmented Mixed-Frequency Approach 临近预测全球经济增长:一个因子增强的混合频率方法

ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic) Pub Date : 2014-10-01 DOI: 10.2139/ssrn.2514218

L. Ferrara, Clément Marsilli

引用次数: 31

An Empirical Investigation of the Value of Finalisation Count Information to Loss Reserving 决算信息对损失准备金价值的实证研究

ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic) Pub Date : 2014-09-01 DOI: 10.2139/ssrn.2613255

G. Taylor, Jing Xu

{"title":"An Empirical Investigation of the Value of Finalisation Count Information to Loss Reserving","authors":"G. Taylor, Jing Xu","doi":"10.2139/ssrn.2613255","DOIUrl":"https://doi.org/10.2139/ssrn.2613255","url":null,"abstract":"The purpose of the present paper has been to test whether loss reserving models that rely on claim count data can produce better forecasts than the chain ladder model (which does not rely on counts); better in the sense of being subject to a lesser prediction error. The question at issue has been tested empirically by reference to the Meyers-Shi data set. Conclusions are drawn on the basis the emerging numerical evidence. The chain ladder is seen as susceptible to forecast error when applied to a portfolio characterised by material changes over time in rates of claim finalisation. For this reason, emphasis has been placed here on the selection of such portfolios for testing. The chain ladder model is applied to a number of portfolios, and so are two other models, the Payments Per Claim Incurred (PPCI) and Payments Per Claim Finalised (PPCF), that rely on claim count data. The latter model in particular is intended to control for changes in finalisation rates. Each model is used to estimate loss reserve and the associated prediction error. A compelling narrative emerges. The chain ladder rarely performs well. Either PPCI or PPCF model produces, or both produce, superior performance, in terms of prediction error, 80% of the time. When the chain ladder produces the best performance of the three models, this appears to be accounted for by either erratic count data or rates of claim finalisation that show comparatively little variation over time.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129614380","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: 6

Score Driven Exponentially Weighted Moving Averages and Value-at-Risk Forecasting 得分驱动指数加权移动平均线和风险价值预测

ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic) Pub Date : 2014-08-19 DOI: 10.2139/ssrn.2470938

A. Lucas, Xin Zhang

引用次数: 69

Forecasting Chinese GDP Growth with Mixed Frequency Data: Which Indicators to Look at? 用混合频率数据预测中国GDP增长:看哪些指标?

ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic) Pub Date : 2014-07-10 DOI: 10.2139/ssrn.2464551

H. Mikosch, Ying Zhang

{"title":"Forecasting Chinese GDP Growth with Mixed Frequency Data: Which Indicators to Look at?","authors":"H. Mikosch, Ying Zhang","doi":"10.2139/ssrn.2464551","DOIUrl":"https://doi.org/10.2139/ssrn.2464551","url":null,"abstract":"Building on a mixed data sampling (MIDAS) model we evaluate the predictive power of a variety of monthly macroeconomic indicators for forecasting quarterly Chinese GDP growth. We iterate the evaluation over forecast horizons from 370 days to 1 day prior to GDP release and track the release days of the indicators so as to only use information which is actually available at the respective day of forecast. This procedure allows us to detect how useful a specific indicator is at a specific forecast horizon relative to other indicators. Despite being published with an (additional) lag of one month the OECD leading indicator outperforms the leading indicators published by the Conference Board and by Goldman Sachs. Albeit being smaller in terms of market volume, the Shenzhen Composite Stock Exchange Index outperforms the Shanghai Composite Stock Exchange Index and several Hong Kong Stock Exchange indices. Consumer price inflation is especially valuable at forecast horizons of 11 to 7 months. The reserve requirement ratio for small banks proves to be a robust predictor at forecast horizons of 9 to 5 months, whereas the big banks reserve requirement ratio and the prime lending rate have lost their leading properties since 2009. Industrial production can be quite valuable for now - or even forecasting, but only if it is released shortly after the end of a month. Neither monthly retail sales, investment, trade, electricity usage, freight traffic nor the manufacturing purchasing managers' index of the Chinese National Bureau of Statistics help much for now - or forecasting. Our results might be relevant for experts who need to know which indicator releases are really valuable for predicting quarterly Chinese GDP growth, and which indicator releases have less predictive content.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127518082","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: 6

Time Varying Transition Probabilities for Markov Regime Switching Models 马尔可夫状态切换模型的时变转移概率

ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic) Pub Date : 2014-06-17 DOI: 10.2139/ssrn.2456632

M. Bazzi, F. Blasques, S. J. Koopman, A. Lucas

引用次数: 2

Forecasting EU Economic Activity Using Financial Condition Indexes 利用财务状况指数预测欧盟经济活动

ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic) Pub Date : 2014-06-01 DOI: 10.2139/SSRN.2444416

G. Kapetanios, Massimiliano Marcellino, Fotis Papailias

引用次数: 3

Finite Sample Weighting of Recursive Forecast Errors 递归预测误差的有限样本加权

ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic) Pub Date : 2014-06-01 DOI: 10.2139/ssrn.2371361

Chris Brooks, S. Burke, Silvia Stanescu

引用次数: 0

Are Professional Forecasters Bayesian? 专业预测师是贝叶斯派吗?

ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic) Pub Date : 2014-05-16 DOI: 10.2139/ssrn.2439444

S. Manzan

引用次数: 7