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.
{"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}
Naive 1 forecasts are often used as a benchmark when assessing the accuracy of a set of forecasts. A ratio is obtained to show the upper bound of a forecasting method's accuracy relative to naive 1 forecasts when the mean squared error is used to measure accuracy. Formulae for the ratio are presented for several exemplar time series processes. The practical use of the ratio as a warning that forecasts have failed to adequately filter the time series signal from the noise is demonstrated.
{"title":"Using Naïve Forecasts to Assess Limits to Forecast Accuracy and the Quality of Fit of Forecasts to Time Series Data","authors":"P. Goodwin","doi":"10.2139/ssrn.2515072","DOIUrl":"https://doi.org/10.2139/ssrn.2515072","url":null,"abstract":"Naive 1 forecasts are often used as a benchmark when assessing the accuracy of a set of forecasts. A ratio is obtained to show the upper bound of a forecasting method's accuracy relative to naive 1 forecasts when the mean squared error is used to measure accuracy. Formulae for the ratio are presented for several exemplar time series processes. The practical use of the ratio as a warning that forecasts have failed to adequately filter the time series signal from the noise is demonstrated.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"abs/2308.00799 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123655369","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}
Facing several economic and financial uncertainties, assessing accurately global economic conditions is a great challenge for economists. The International Monetary Fund proposes within its periodic World Economic Outlook report a measure of the global GDP annual growth, that is often considered as the benchmark nowcast by macroeconomists. In this paper, we put forward an alternative approach to provide monthly nowcasts of the annual global growth rate. Our approach builds on a Factor-Augmented MIxed DAta Sampling (FA-MIDAS) model that enables (i) to account for a large monthly database including various countries and sectors of the global economy and (ii) to nowcast a low-frequency macroeconomic variable using higher-frequency information. Pseudo real-time results show that this approach provides reliable and timely nowcasts of the world GDP annual growth on a monthly basis.
{"title":"Nowcasting Global Economic Growth: A Factor-Augmented Mixed-Frequency Approach","authors":"L. Ferrara, Clément Marsilli","doi":"10.2139/ssrn.2514218","DOIUrl":"https://doi.org/10.2139/ssrn.2514218","url":null,"abstract":"Facing several economic and financial uncertainties, assessing accurately global economic conditions is a great challenge for economists. The International Monetary Fund proposes within its periodic World Economic Outlook report a measure of the global GDP annual growth, that is often considered as the benchmark nowcast by macroeconomists. In this paper, we put forward an alternative approach to provide monthly nowcasts of the annual global growth rate. Our approach builds on a Factor-Augmented MIxed DAta Sampling (FA-MIDAS) model that enables (i) to account for a large monthly database including various countries and sectors of the global economy and (ii) to nowcast a low-frequency macroeconomic variable using higher-frequency information. Pseudo real-time results show that this approach provides reliable and timely nowcasts of the world GDP annual growth on a monthly basis.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124447509","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 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.
{"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}
A simple methodology is presented for modeling time variation in volatilities and other higher order moments using a recursive updating scheme similar to the familiar RiskMetrics approach. We update parameters using the score of the forecasting distribution. This allows the parameter dynamics to adapt automatically to any non-normal data features and robustifies the subsequent estimates. The new approach nests several of the earlier extensions to the exponentially weighted moving average (EWMA) scheme. In addition, it can easily be extended to higher dimensions and alternative forecasting distributions. The method is applied to Value-at-Risk forecasting with (skewed) Student's t distributions and a time-varying degrees of freedom and/or skewness parameter. We show that the new method is competitive to or better than earlier methods in forecasting volatility of individual stock returns and exchange rate returns.
{"title":"Score Driven Exponentially Weighted Moving Averages and Value-at-Risk Forecasting","authors":"A. Lucas, Xin Zhang","doi":"10.2139/ssrn.2470938","DOIUrl":"https://doi.org/10.2139/ssrn.2470938","url":null,"abstract":"A simple methodology is presented for modeling time variation in volatilities and other higher order moments using a recursive updating scheme similar to the familiar RiskMetrics approach. We update parameters using the score of the forecasting distribution. This allows the parameter dynamics to adapt automatically to any non-normal data features and robustifies the subsequent estimates. The new approach nests several of the earlier extensions to the exponentially weighted moving average (EWMA) scheme. In addition, it can easily be extended to higher dimensions and alternative forecasting distributions. The method is applied to Value-at-Risk forecasting with (skewed) Student's t distributions and a time-varying degrees of freedom and/or skewness parameter. We show that the new method is competitive to or better than earlier methods in forecasting volatility of individual stock returns and exchange rate returns.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130461981","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}
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.
在混合数据抽样(MIDAS)模型的基础上，我们评估了各种月度宏观经济指标对中国季度GDP增长的预测能力。我们在GDP发布前370天至1天的预测范围内进行迭代评估，并跟踪指标的发布日期，以便仅使用预测当天实际可用的信息。这一过程使我们能够检测一个特定指标相对于其他指标在特定预测范围内的有用程度。尽管经合组织领先指标的发布(又)滞后一个月，但其表现优于世界大型企业联合会(Conference Board)和高盛(Goldman Sachs)发布的领先指标。虽然市场规模较小，但深证综合指数的表现优于上证综合指数和数个香港证券交易所指数。在11至7个月的预测期内，消费者价格通胀尤其有价值。事实证明，在9至5个月的预测期内，小银行的存款准备金率是一个强有力的预测指标，而大银行的存款准备金率和优惠贷款利率自2009年以来已失去了主导作用。目前，工业生产数据可能很有价值，甚至预测也很有价值，但前提是该数据必须在月底后不久发布。无论是月度零售额、投资、贸易、用电量、货运量，还是中国国家统计局(National Bureau of Statistics)的制造业采购经理人指数(pmi)，目前都没有多大帮助，预测也没有。对于那些需要知道哪些指标发布对预测中国季度GDP增长真正有价值，哪些指标发布的预测内容较少的专家来说，我们的结果可能是相关的。
{"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}
We propose a new Markov switching model with time varying probabilities for the transitions. The novelty of our model is that the transition probabilities evolve over time by means of an observation driven model. The innovation of the time varying probability is generated by the score of the predictive likelihood function. We show how the model dynamics can be readily interpreted. We investigate the performance of the model in a Monte Carlo study and show that the model is successful in estimating a range of different dynamic patterns for unobserved regime switching probabilities. We also illustrate the new methodology in an empirical setting by studying the dynamic mean and variance behavior of U.S. Industrial Production growth. We find empirical evidence of changes in the regime switching probabilities, with more persistence for high volatility regimes in the earlier part of the sample, and more persistence for low volatility regimes in the later part of the sample.
{"title":"Time Varying Transition Probabilities for Markov Regime Switching Models","authors":"M. Bazzi, F. Blasques, S. J. Koopman, A. Lucas","doi":"10.2139/ssrn.2456632","DOIUrl":"https://doi.org/10.2139/ssrn.2456632","url":null,"abstract":"We propose a new Markov switching model with time varying probabilities for the transitions. The novelty of our model is that the transition probabilities evolve over time by means of an observation driven model. The innovation of the time varying probability is generated by the score of the predictive likelihood function. We show how the model dynamics can be readily interpreted. We investigate the performance of the model in a Monte Carlo study and show that the model is successful in estimating a range of different dynamic patterns for unobserved regime switching probabilities. We also illustrate the new methodology in an empirical setting by studying the dynamic mean and variance behavior of U.S. Industrial Production growth. We find empirical evidence of changes in the regime switching probabilities, with more persistence for high volatility regimes in the earlier part of the sample, and more persistence for low volatility regimes in the later part of the sample.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126639948","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}
G. Kapetanios, Massimiliano Marcellino, Fotis Papailias
This paper investigates the performance of Financial Condition Indexes (FCIs) in forecasting four key macroeconomic variables of EU economies. A wide range of carefully selected financial indicators include Rates and Spreads, Stock Market Indicators and Macroeconomic Quantities. The results provide evidence that FCIs are particularly useful in forecasting GDP growth, Consumption growth, Industrial Production growth and the Unemployment Rate.
{"title":"Forecasting EU Economic Activity Using Financial Condition Indexes","authors":"G. Kapetanios, Massimiliano Marcellino, Fotis Papailias","doi":"10.2139/SSRN.2444416","DOIUrl":"https://doi.org/10.2139/SSRN.2444416","url":null,"abstract":"This paper investigates the performance of Financial Condition Indexes (FCIs) in forecasting four key macroeconomic variables of EU economies. A wide range of carefully selected financial indicators include Rates and Spreads, Stock Market Indicators and Macroeconomic Quantities. The results provide evidence that FCIs are particularly useful in forecasting GDP growth, Consumption growth, Industrial Production growth and the Unemployment Rate.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123743258","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 paper proposes and tests a new framework for weighting recursive out-of-sample prediction errors according to their corresponding levels of in-sample estimation uncertainty. In essence, we show how to use the maximum possible amount of information from the sample in the evaluation of the prediction accuracy, by commencing the forecasts at the earliest opportunity and weighting the prediction errors. Via a Monte Carlo study, we demonstrate that the proposed framework selects the correct model from a set of candidate models considerably more often than the existing standard approach when only a small sample is available. We also show that the proposed weighting approaches result in tests of equal predictive accuracy that have much better sizes than the standard approach. An application to an exchange rate dataset highlights relevant differences in the results of tests of predictive accuracy based on the standard approach versus the framework proposed in this paper.
{"title":"Finite Sample Weighting of Recursive Forecast Errors","authors":"Chris Brooks, S. Burke, Silvia Stanescu","doi":"10.2139/ssrn.2371361","DOIUrl":"https://doi.org/10.2139/ssrn.2371361","url":null,"abstract":"This paper proposes and tests a new framework for weighting recursive out-of-sample prediction errors according to their corresponding levels of in-sample estimation uncertainty. In essence, we show how to use the maximum possible amount of information from the sample in the evaluation of the prediction accuracy, by commencing the forecasts at the earliest opportunity and weighting the prediction errors. Via a Monte Carlo study, we demonstrate that the proposed framework selects the correct model from a set of candidate models considerably more often than the existing standard approach when only a small sample is available. We also show that the proposed weighting approaches result in tests of equal predictive accuracy that have much better sizes than the standard approach. An application to an exchange rate dataset highlights relevant differences in the results of tests of predictive accuracy based on the standard approach versus the framework proposed in this paper.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121762162","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}
I evaluate whether expectations of professional forecasters are consistent with the property of Bayesian learning that the expected uncertainty of a fixed target forecast should decline with the horizon. I obtain a measure of individual uncertainty from the density forecasts of the Survey of Professional Forecasters (SPF) and the ECB-SPF and use it to test the prediction of the learning model. Empirically, I find that the prediction is often violated, in particular when forecasters experience unexpected news in the most recent data release, and following quarters in which they produce narrow forecasts. In addition, I find significant heterogeneity in the updating behavior of forecasters in response to changes in these variables.
{"title":"Are Professional Forecasters Bayesian?","authors":"S. Manzan","doi":"10.2139/ssrn.2439444","DOIUrl":"https://doi.org/10.2139/ssrn.2439444","url":null,"abstract":"I evaluate whether expectations of professional forecasters are consistent with the property of Bayesian learning that the expected uncertainty of a fixed target forecast should decline with the horizon. I obtain a measure of individual uncertainty from the density forecasts of the Survey of Professional Forecasters (SPF) and the ECB-SPF and use it to test the prediction of the learning model. Empirically, I find that the prediction is often violated, in particular when forecasters experience unexpected news in the most recent data release, and following quarters in which they produce narrow forecasts. In addition, I find significant heterogeneity in the updating behavior of forecasters in response to changes in these variables.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127034257","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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