While combining forecasts is well-known to reduce error, the question of how to best combine forecasts remains. Prior research suggests that combining is most beneficial when relying on diverse forecasts that incorporate different information. Here I provide evidence in support of this hypothesis by analyzing data from the PollyVote project, which has published combined forecasts of the popular vote in U.S. presidential elections since 2004. Prior to the 2020 election, the PollyVote revised its original method of combining forecasts by, first, restructuring individual forecasts based on their underlying information and, second, adding naïve forecasts as a new component method. On average across the last 100 days prior to the five elections from 2004 to 2020, the revised PollyVote reduced the error of the original specification by eight percent and, with a mean absolute error of 0.8 percentage points, was more accurate than any of its component forecasts. The results suggest that, when deciding about which forecasts to include in the combination, forecasters should be more concerned about the component forecasts’ diversity than their historical accuracy.
{"title":"Embrace the Differences: Revisiting the Pollyvote Method of Combining Forecasts for U.S. Presidential Elections (2004 to 2020)","authors":"A. Graefe","doi":"10.2139/ssrn.3871059","DOIUrl":"https://doi.org/10.2139/ssrn.3871059","url":null,"abstract":"While combining forecasts is well-known to reduce error, the question of how to best combine forecasts remains. Prior research suggests that combining is most beneficial when relying on diverse forecasts that incorporate different information. Here I provide evidence in support of this hypothesis by analyzing data from the PollyVote project, which has published combined forecasts of the popular vote in U.S. presidential elections since 2004. Prior to the 2020 election, the PollyVote revised its original method of combining forecasts by, first, restructuring individual forecasts based on their underlying information and, second, adding naïve forecasts as a new component method. On average across the last 100 days prior to the five elections from 2004 to 2020, the revised PollyVote reduced the error of the original specification by eight percent and, with a mean absolute error of 0.8 percentage points, was more accurate than any of its component forecasts. The results suggest that, when deciding about which forecasts to include in the combination, forecasters should be more concerned about the component forecasts’ diversity than their historical accuracy.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122020618","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 novel token-distance-based triple approach is proposed for identifying EPU mentions in textual documents. The method is applied to a corpus of French-language news to construct a century-long historical EPU index for the Canadian province of Quebec. The relevance of the index is shown in a macroeconomic nowcasting experiment.
{"title":"A Century of Economic Policy Uncertainty Through the French-Canadian Lens","authors":"David Ardia, Keven Bluteau, Alaa Kassem","doi":"10.2139/ssrn.3773702","DOIUrl":"https://doi.org/10.2139/ssrn.3773702","url":null,"abstract":"A novel token-distance-based triple approach is proposed for identifying EPU mentions in textual documents. The method is applied to a corpus of French-language news to construct a century-long historical EPU index for the Canadian province of Quebec. The relevance of the index is shown in a macroeconomic nowcasting experiment.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126085704","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}
Giovanni Angelini, Luca De Angelis, Carl Singleton
Abstract Studies of financial market informational efficiency have proven burdensome in practice, because it is difficult to pinpoint when news breaks and is known by some or all the participants. We overcome this by designing a framework to detect mispricing, test informational efficiency and evaluate the behavioural biases within high-frequency prediction markets. We demonstrate this using betting exchange data for association football, exploiting the moment when the first goal is scored in a match as major news that breaks cleanly. There are pre-match and in-play mispricing and inefficiency in these markets, explained by reverse favourite-longshot bias (favourite bias). The mispricing tends to increase when the major news is a surprise, such as a goal scored by a longshot team late in a match, with the market underestimating their chances of going on to win These results suggest that, even in prediction markets with large crowds of participants trading state-contingent claims, significant informational inefficiency and behavioural biases can be reflected in prices.
{"title":"Informational Efficiency and Behaviour Within In-Play Prediction Markets","authors":"Giovanni Angelini, Luca De Angelis, Carl Singleton","doi":"10.2139/ssrn.3505287","DOIUrl":"https://doi.org/10.2139/ssrn.3505287","url":null,"abstract":"Abstract Studies of financial market informational efficiency have proven burdensome in practice, because it is difficult to pinpoint when news breaks and is known by some or all the participants. We overcome this by designing a framework to detect mispricing, test informational efficiency and evaluate the behavioural biases within high-frequency prediction markets. We demonstrate this using betting exchange data for association football, exploiting the moment when the first goal is scored in a match as major news that breaks cleanly. There are pre-match and in-play mispricing and inefficiency in these markets, explained by reverse favourite-longshot bias (favourite bias). The mispricing tends to increase when the major news is a surprise, such as a goal scored by a longshot team late in a match, with the market underestimating their chances of going on to win These results suggest that, even in prediction markets with large crowds of participants trading state-contingent claims, significant informational inefficiency and behavioural biases can be reflected in prices.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125101193","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 introduces a new class of observation-driven models, including score models as a special case. This new class inherits and extends the basic ideas behind the development of score models and addresses a number of unsolved issues in the score literature. In particular, the new class of models (i) allows QML estimation of static parameters, (ii) allows the production of leverage effects in the presence of negative outliers, (iii) allows update asymmetry and asymmetric forecast loss functions in the presence of symmetric or skewed innovations, and (iii) achieves out-of-sample outlier robustness in the presence of sub-exponential tails. We establish the asymptotic properties of the QLE, QMLE, and MLE as well as likelihood ratio and Lagrange multiplier test statistics. The finite sample properties are studied by means of an extensive Monte Carlo study. Finally, we show the empirical relevance of this new class of models on real data.
{"title":"A New Class of Robust Observation-Driven Models","authors":"F. Blasques, C. Francq, S. Laurent","doi":"10.2139/ssrn.3716133","DOIUrl":"https://doi.org/10.2139/ssrn.3716133","url":null,"abstract":"This paper introduces a new class of observation-driven models, including score models as a special case. This new class inherits and extends the basic ideas behind the development of score models and addresses a number of unsolved issues in the score literature. In particular, the new class of models (i) allows QML estimation of static parameters, (ii) allows the production of leverage effects in the presence of negative outliers, (iii) allows update asymmetry and asymmetric forecast loss functions in the presence of symmetric or skewed innovations, and (iii) achieves out-of-sample outlier robustness in the presence of sub-exponential tails. We establish the asymptotic properties of the QLE, QMLE, and MLE as well as likelihood ratio and Lagrange multiplier test statistics. The finite sample properties are studied by means of an extensive Monte Carlo study. Finally, we show the empirical relevance of this new class of models on real data.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126584552","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 research work, we discuss Nigerian stock price and model it using Variance-Gamma distribution. We compare the model with closely related distributions and test the goodness of fit. Finally, we compare Nigerian stock price model with Johannesburg stock exchange model.
{"title":"Modelling and Forecasting of the Nigerian Stock Exchange.","authors":"Ibraheem Abiodun Yahayah","doi":"10.2139/ssrn.3836115","DOIUrl":"https://doi.org/10.2139/ssrn.3836115","url":null,"abstract":"In this research work, we discuss Nigerian stock price and model it using<br>Variance-Gamma distribution. We compare the model with closely related<br>distributions and test the goodness of fit. Finally, we compare Nigerian stock<br>price model with Johannesburg stock exchange model.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128087713","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. Filippou, D. Rapach, Mark P. Taylor, Guofu Zhou
We establish the out-of-sample predictability of monthly exchange rate changes via machine learning techniques based on 70 predictors capturing country characteristics, global variables, and their interactions. To guard against overfi tting, we use the elastic net to estimate a high-dimensional panel predictive regression and find that the resulting forecast consistently outperforms the naive no-change benchmark, which has proven difficult to beat in the literature. The forecast also markedly improves the performance of a carry trade portfolio, especially during and after the global financial crisis. When we allow for more complex deep learning models, nonlinearities do not appear substantial in the data.
{"title":"Exchange Rate Prediction with Machine Learning and a Smart Carry Portfolio","authors":"I. Filippou, D. Rapach, Mark P. Taylor, Guofu Zhou","doi":"10.2139/ssrn.3455713","DOIUrl":"https://doi.org/10.2139/ssrn.3455713","url":null,"abstract":"We establish the out-of-sample predictability of monthly exchange rate changes via machine learning techniques based on 70 predictors capturing country characteristics, global variables, and their interactions. To guard against overfi tting, we use the elastic net to estimate a high-dimensional panel predictive regression and find that the resulting forecast consistently outperforms the naive no-change benchmark, which has proven difficult to beat in the literature. The forecast also markedly improves the performance of a carry trade portfolio, especially during and after the global financial crisis. When we allow for more complex deep learning models, nonlinearities do not appear substantial in the data.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"2011 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121503438","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 The aim of the paper is to compare the forecasting performance of a class of statedependent autoregressive (SDAR) models for univariate time series with two alternative families of nonlinear models, such as the SETAR and the GARCH models. The study is conducted on US GDP growth rate using quarterly data. Two methods of forecast comparison are employed. The first method consists in evaluation the average performance by using two measures such as the root mean square error (RMSE) and the mean absolute error (MAE) over different forecast horizons, while the second method make use of one of the most used statistical test to compare the accuracy of two forecast methods such as the Diebold-Mariano test. JEL classification numbers: C22, E37, F47. Keywords: Nonlinear models for time series, GDP growth rate, Forecasting accuracy.
{"title":"Evaluating Forecasts from State-Dependent Autoregressive Models for US GDP Growth Rate. Comparison with Alternative Approaches","authors":"F. Gobbi","doi":"10.2139/ssrn.3641831","DOIUrl":"https://doi.org/10.2139/ssrn.3641831","url":null,"abstract":"Abstract\u0000\u0000The aim of the paper is to compare the forecasting performance of a class of statedependent autoregressive (SDAR) models for univariate time series with two\u0000alternative families of nonlinear models, such as the SETAR and the GARCH\u0000models. The study is conducted on US GDP growth rate using quarterly data. Two\u0000methods of forecast comparison are employed. The first method consists in\u0000evaluation the average performance by using two measures such as the root mean\u0000square error (RMSE) and the mean absolute error (MAE) over different forecast\u0000horizons, while the second method make use of one of the most used statistical test\u0000to compare the accuracy of two forecast methods such as the Diebold-Mariano test.\u0000\u0000JEL classification numbers: C22, E37, F47.\u0000Keywords: Nonlinear models for time series, GDP growth rate, Forecasting\u0000accuracy.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133095945","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}
S. Majumder, Md. Hasanur Rahman, Frajana Layla, Mohammad Zoynul Abedin
This study aims to analyse the COVID-19 impact on remittance inflow in the selected south Asian (developing) countries. The current study applies the Automatic ARIMA forecasting by using the ARMA method up to 2021M12. The major findings demonstrate that the remittances inflow in selected countries has faced with negative and zero growth rates due to lockdown situations in wage earners markets. At the end of 2019M12, the economy gets the growth rate of remittance inflows in Bangladesh is 0.08%, Sri Lanka is 0.29%, and Pakistan is 0.15%. At the early of 2020M02, growth rate has -0.11%, -0.09% and -0.04%, respectively. The estimated remittances growth rate at the end of 2020M12 is 0.10%, -0.002%, and 0.03%, respectively. This study adds extensive knowledge and importance of wage earners' remittances inflows with problem and solutions aspects. Accordingly, we will add some new points to the existing literature.
{"title":"Forecasting the Impact of COVID-19 on Remittance Inflows in Selected South Asian Countries","authors":"S. Majumder, Md. Hasanur Rahman, Frajana Layla, Mohammad Zoynul Abedin","doi":"10.2139/ssrn.3648937","DOIUrl":"https://doi.org/10.2139/ssrn.3648937","url":null,"abstract":"This study aims to analyse the COVID-19 impact on remittance inflow in the selected south Asian (developing) countries. The current study applies the Automatic ARIMA forecasting by using the ARMA method up to 2021M12. The major findings demonstrate that the remittances inflow in selected countries has faced with negative and zero growth rates due to lockdown situations in wage earners markets. At the end of 2019M12, the economy gets the growth rate of remittance inflows in Bangladesh is 0.08%, Sri Lanka is 0.29%, and Pakistan is 0.15%. At the early of 2020M02, growth rate has -0.11%, -0.09% and -0.04%, respectively. The estimated remittances growth rate at the end of 2020M12 is 0.10%, -0.002%, and 0.03%, respectively. This study adds extensive knowledge and importance of wage earners' remittances inflows with problem and solutions aspects. Accordingly, we will add some new points to the existing literature.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114247905","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 We conduct a sentiment analysis of the FOMC (Federal Open Market Committee) minutes based on the text mining results and examine the predictive ability of the resulting sentiment indicators. An adaptive Bayesian approach is employed to build the sentiment indicator for each of the Fed's mandates. We also improve existing mining techniques by identifying economics-related compound words and terminology in the minutes. Our empirical study shows that the mandate-specific indicators exhibit distinct patterns which help illustrate the FOMC's policy emphasis in different periods. It is also shown that these indicators are useful in predicting economic variables and generating superior out-of-sample forecasts. These results support the existing findings that the Fed possesses valuable information about the U.S. economy.
{"title":"Economic Prediction with the FOMC Minutes: An Application of Text Mining","authors":"Yu-Lieh Huang, Chung-Ming Kuan","doi":"10.2139/ssrn.3534914","DOIUrl":"https://doi.org/10.2139/ssrn.3534914","url":null,"abstract":"Abstract We conduct a sentiment analysis of the FOMC (Federal Open Market Committee) minutes based on the text mining results and examine the predictive ability of the resulting sentiment indicators. An adaptive Bayesian approach is employed to build the sentiment indicator for each of the Fed's mandates. We also improve existing mining techniques by identifying economics-related compound words and terminology in the minutes. Our empirical study shows that the mandate-specific indicators exhibit distinct patterns which help illustrate the FOMC's policy emphasis in different periods. It is also shown that these indicators are useful in predicting economic variables and generating superior out-of-sample forecasts. These results support the existing findings that the Fed possesses valuable information about the U.S. economy.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"194 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131923766","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}
Many applications call for measuring the response due to shocks from several variables at once. We introduce a joint impulse response function (jIRF) that is independent of the order of the variables and allows for simultaneous shocks from multiple variables in the VAR, rather than one at a time as in the generalized IRF. The proposed jIRF controls for the cross-correlations of the several simultaneous shocks. As an application of the jIRF, we study the effect of the COVID-19 pandemic on trans-Atlantic volatility transmissions across large financial institutions and show that simply summing the generalized IRFs overestimates volatility transmissions.
{"title":"A Joint Impulse Response Function for Vector Autoregressive Models","authors":"Thomas F. P. Wiesen, Paul M. Beaumont","doi":"10.2139/ssrn.3518133","DOIUrl":"https://doi.org/10.2139/ssrn.3518133","url":null,"abstract":"Many applications call for measuring the response due to shocks from several variables at once. We introduce a joint impulse response function (jIRF) that is independent of the order of the variables and allows for simultaneous shocks from multiple variables in the VAR, rather than one at a time as in the generalized IRF. The proposed jIRF controls for the cross-correlations of the several simultaneous shocks. As an application of the jIRF, we study the effect of the COVID-19 pandemic on trans-Atlantic volatility transmissions across large financial institutions and show that simply summing the generalized IRFs overestimates volatility transmissions.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122208433","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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