We propose an approach for generating macroeconomic density forecasts that incorporate information on multiple scenarios defined by experts. We adopt a regime-switching framework in which sets of scenarios (“views”) are used as Bayesian priors on economic regimes. Predictive densities coming from different views are then combined by optimizing objective functions of density forecasting. We illustrate the approach with an empirical application to quarterly real-time forecasts of the US GDP growth rate, in which we exploit the Fed's macroeconomic scenarios used for bank stress tests. We show that the approach achieves good accuracy in terms of average predictive scores and good calibration of forecast distributions. Moreover, it can be used to evaluate the contribution of economists' scenarios to density forecast performance.
{"title":"Regime-Switching Density Forecasts Using Economists' Scenarios","authors":"Graziano Moramarco","doi":"10.1002/for.3228","DOIUrl":"https://doi.org/10.1002/for.3228","url":null,"abstract":"<p>We propose an approach for generating macroeconomic density forecasts that incorporate information on multiple scenarios defined by experts. We adopt a regime-switching framework in which sets of scenarios (“views”) are used as Bayesian priors on economic regimes. Predictive densities coming from different views are then combined by optimizing objective functions of density forecasting. We illustrate the approach with an empirical application to quarterly real-time forecasts of the US GDP growth rate, in which we exploit the Fed's macroeconomic scenarios used for bank stress tests. We show that the approach achieves good accuracy in terms of average predictive scores and good calibration of forecast distributions. Moreover, it can be used to evaluate the contribution of economists' scenarios to density forecast performance.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 2","pages":"833-845"},"PeriodicalIF":3.4,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/for.3228","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143119266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Belen Chocobar, Peter Claeys, Marcos Poplawski-Ribeiro
Macroeconomic theories attribute rigidities in expectations formation to two mechanisms: sticky or noisy information. Recent advances in testing time variations in forecast dispersion—using the fluctuation rationality test—allow detecting departures from forecaster rationality over time. Relating individual forecaster behavior to economic or political factors on a panel of budget balance forecasts from Consensus Economics, a large panel of individual expert forecasters in four major OECD countries between 1993 to 2023, we find evidence for forecaster behavior in line with noisy information. Traditional full-sample tests show that forecasters are not rational, but this is due to an overly pessimistic reaction to sudden big shifts, like the global financial crisis or the pandemic. In normal times, forecasters do systematically incorporate economic and political news in budget forecast revisions.
{"title":"Fiscal Forecasting Rationality Among Expert Forecasters","authors":"Belen Chocobar, Peter Claeys, Marcos Poplawski-Ribeiro","doi":"10.1002/for.3237","DOIUrl":"https://doi.org/10.1002/for.3237","url":null,"abstract":"<p>Macroeconomic theories attribute rigidities in expectations formation to two mechanisms: sticky or noisy information. Recent advances in testing time variations in forecast dispersion—using the fluctuation rationality test—allow detecting departures from forecaster rationality over time. Relating individual forecaster behavior to economic or political factors on a panel of budget balance forecasts from Consensus Economics, a large panel of individual expert forecasters in four major OECD countries between 1993 to 2023, we find evidence for forecaster behavior in line with noisy information. Traditional full-sample tests show that forecasters are not rational, but this is due to an overly pessimistic reaction to sudden big shifts, like the global financial crisis or the pandemic. In normal times, forecasters do systematically incorporate economic and political news in budget forecast revisions.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 3","pages":"941-959"},"PeriodicalIF":3.4,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/for.3237","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143565436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study modifies a wage–price-setting (WPS) model to forecast US inflation over 1- to 3-year horizons, based on the assumption that firms use a rule of thumb to set prices after settling a wage agreement. The out-of-sample forecast results show that productivity growth is a powerful predictor of inflation, in the sense that during the 1990Q1–2023Q4 period, the modified WPS model improved upon some univariate benchmark models and multivariate models such as the Phillips curve, term spread, and wage-inflation models. From the early 2000s to the prepandemic period, forecast accuracy was improved by combining productivity growth with anchored inflation expectations. Interestingly, during this period, forecasts derived from the WPS model with constant-inflation expectations were found to slightly outperform Greenbook forecasts in forecasting quarter-over-quarter inflation from two- to four-quarter horizons.
{"title":"Using a Wage–Price-Setting Model to Forecast US Inflation","authors":"Nguyen Duc Do","doi":"10.1002/for.3210","DOIUrl":"https://doi.org/10.1002/for.3210","url":null,"abstract":"<div>\u0000 \u0000 <p>This study modifies a wage–price-setting (WPS) model to forecast US inflation over 1- to 3-year horizons, based on the assumption that firms use a rule of thumb to set prices after settling a wage agreement. The out-of-sample forecast results show that productivity growth is a powerful predictor of inflation, in the sense that during the 1990Q1–2023Q4 period, the modified WPS model improved upon some univariate benchmark models and multivariate models such as the Phillips curve, term spread, and wage-inflation models. From the early 2000s to the prepandemic period, forecast accuracy was improved by combining productivity growth with anchored inflation expectations. Interestingly, during this period, forecasts derived from the WPS model with constant-inflation expectations were found to slightly outperform Greenbook forecasts in forecasting quarter-over-quarter inflation from two- to four-quarter horizons.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 2","pages":"803-832"},"PeriodicalIF":3.4,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143118143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
For a very long time, bankruptcy models were considered ahistorical, as they were mostly based on ratios measured over a single year. However, time is an essential variable that explains a firm's ability to survive. It is precisely for these reasons that measures intended to represent firm history have been studied and progressively used to complement traditional explanatory variables using financial ratios or variation indicators of such ratios. Even if these measures are not totally useless, they failed to be widely used in the literature. This is the reason why we propose a method, called temporal financial pattern–based method (TPM) that makes it possible to efficiently represent a firm's history using a quantification process and use the result of this process to improve model accuracy. This method relies on an estimation of typical temporal financial patterns that govern changes in a firm's financial situation over time, using neural networks. The results demonstrate that TPM leads to better prediction accuracy than that achieved with traditional models.
{"title":"A Quantification Approach of Changes in Firms' Financial Situation Using Neural Networks for Predicting Bankruptcy","authors":"Philippe du Jardin","doi":"10.1002/for.3227","DOIUrl":"https://doi.org/10.1002/for.3227","url":null,"abstract":"<div>\u0000 \u0000 <p>For a very long time, bankruptcy models were considered ahistorical, as they were mostly based on ratios measured over a single year. However, time is an essential variable that explains a firm's ability to survive. It is precisely for these reasons that measures intended to represent firm history have been studied and progressively used to complement traditional explanatory variables using financial ratios or variation indicators of such ratios. Even if these measures are not totally useless, they failed to be widely used in the literature. This is the reason why we propose a method, called temporal financial pattern–based method (TPM) that makes it possible to efficiently represent a firm's history using a quantification process and use the result of this process to improve model accuracy. This method relies on an estimation of typical temporal financial patterns that govern changes in a firm's financial situation over time, using neural networks. The results demonstrate that TPM leads to better prediction accuracy than that achieved with traditional models.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 2","pages":"781-802"},"PeriodicalIF":3.4,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143117895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study examines how global risk aversion affects future real economic activity (REA). We propose a new international real business cycle (RBC) framework with a stochastic global risk aversion spillover process by extending the RBC model. Our model suggests output competition and risk aversion spillover as two influence channels of global risk aversion. We extract relative risk aversion factors and evaluate the significance of changes in the level of global risk aversion for forecasting. Our findings suggest that changes in the level of global risk aversion significantly drive the business cycle of open economies. A global risk aversion factor predicts a domestic country's future REA at least as well as the domestic risk aversion factor does. The impact of global risk aversion can vary depending on a country's relative productivity.
{"title":"Global Risk Aversion: Driving Force of Future Real Economic Activity","authors":"Jinhwan Kim, Hoon Cho, Doojin Ryu","doi":"10.1002/for.3203","DOIUrl":"https://doi.org/10.1002/for.3203","url":null,"abstract":"<p>This study examines how global risk aversion affects future real economic activity (REA). We propose a new international real business cycle (RBC) framework with a stochastic global risk aversion spillover process by extending the RBC model. Our model suggests output competition and risk aversion spillover as two influence channels of global risk aversion. We extract relative risk aversion factors and evaluate the significance of changes in the level of global risk aversion for forecasting. Our findings suggest that changes in the level of global risk aversion significantly drive the business cycle of open economies. A global risk aversion factor predicts a domestic country's future REA at least as well as the domestic risk aversion factor does. The impact of global risk aversion can vary depending on a country's relative productivity.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 2","pages":"706-729"},"PeriodicalIF":3.4,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/for.3203","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143117892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
<div> <p>Crowd flow forecasting is vital for urban planning, resource allocation, and public safety, particularly in the context of the COVID-19 pandemic. However, traditional predictive models struggle to capture the complex, nonlinear spatial–temporal relationships inherent in crowd flow data due to its irregular volatility. To address these limitations, this paper proposes the innovative citywide crowd flow prediction (CCFP) model, which merges statistical rules with machine learning techniques (XGBoost, LightGBM, and CatBoost). The CCFP model is specifically designed to leverage spatial dependencies and two-level periodicity (weekly and daily) in population flow to predict crowd flow indexes ( <span></span><math> <semantics> <mrow> <mi>C</mi> <mi>F</mi> <mi>I</mi> </mrow> <annotation>$$ CFI $$</annotation> </semantics></math>) within specific areas. We employ an urban area graph created using the Node2Vec algorithm to capture the temporal and spatial nuances of human flow patterns. Notably, this study innovatively incorporates migration, weather, and epidemic data into machine-learning models for feature extraction. Moreover, it introduces weighted factors— <span></span><math> <semantics> <mrow> <mi>g</mi> <mi>r</mi> <mi>o</mi> <mi>w</mi> <mi>t</mi> <mi>h</mi> <mo>,</mo> <mspace></mspace> <mi>b</mi> <mi>a</mi> <mi>s</mi> <mi>e</mi> <mo>,</mo> <mspace></mspace> <mi>w</mi> <mi>e</mi> <mi>e</mi> <mi>k</mi> </mrow> <annotation>$$ growth, base, week $$</annotation> </semantics></math>, and <span></span><math> <semantics> <mrow> <mi>d</mi> <mi>a</mi> <mi>y</mi> </mrow> <annotation>$$ day $$</annotation> </semantics></math>—to enhance the accuracy of <span></span><math> <semantics> <mrow> <mi>C</mi> <mi>F</mi> <mi>I</mi> </mrow> <annotation>$$ CFI $$</annotation> </semantics></math> prediction. Among the combined models, CCFP outperforms others with remarkable scientific precision (root mean squared error, <span></span><math> <semantics> <mrow> <mi>R</mi> <mi>M</mi> <mi>S</mi> <mi>E</mi> <mo>=</mo> <mn>2.04</mn> </mrow>
人流预测对城市规划、资源分配和公共安全至关重要，尤其是在 COVID-19 大流行的背景下。然而，由于人流数据的不规则波动性，传统的预测模型难以捕捉人流数据中固有的复杂、非线性时空关系。为了解决这些局限性，本文提出了创新性的全市人群流量预测模型（CCFP），该模型融合了统计规则和机器学习技术（XGBoost、LightGBM 和 CatBoost）。CCFP 模型专门设计用于利用人口流动的空间依赖性和两级周期性（每周和每天）来预测特定区域内的人流指数（C F I $$ CFI $$）。我们采用 Node2Vec 算法创建的城市区域图来捕捉人流模式在时间和空间上的细微差别。值得注意的是，本研究创新性地将移民、天气和流行病数据纳入机器学习模型，以提取特征。此外，它还引入了加权因子--增长、基数、周$$和日$$，以提高 C F I $$ CFI $$预测的准确性。在组合模型中，CCFP 以显著的科学精度（均方根误差，R M S E = 2.04 $$ RMSE=2.04 $$；平均绝对误差，M A E = 0.81 $$ MAE=0.81 $$；平均绝对百分比误差，M A P E = 0.13 $$ MAPE=0.13 $$）优于其他模型。总之，CCFP 模型代表了人流预测领域的重大进步，为大流行病期间的城市安全管理和城市规划提供了宝贵的见解。
{"title":"Data-Driven Predictive Modeling of Citywide Crowd Flow for Urban Safety Management: A Case Study of Beijing, China","authors":"He Jiang, Xuxilu Zhang, Yao Dong, Jianzhou Wang","doi":"10.1002/for.3216","DOIUrl":"https://doi.org/10.1002/for.3216","url":null,"abstract":"<div>\u0000 \u0000 <p>Crowd flow forecasting is vital for urban planning, resource allocation, and public safety, particularly in the context of the COVID-19 pandemic. However, traditional predictive models struggle to capture the complex, nonlinear spatial–temporal relationships inherent in crowd flow data due to its irregular volatility. To address these limitations, this paper proposes the innovative citywide crowd flow prediction (CCFP) model, which merges statistical rules with machine learning techniques (XGBoost, LightGBM, and CatBoost). The CCFP model is specifically designed to leverage spatial dependencies and two-level periodicity (weekly and daily) in population flow to predict crowd flow indexes (\u0000<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>C</mi>\u0000 <mi>F</mi>\u0000 <mi>I</mi>\u0000 </mrow>\u0000 <annotation>$$ CFI $$</annotation>\u0000 </semantics></math>) within specific areas. We employ an urban area graph created using the Node2Vec algorithm to capture the temporal and spatial nuances of human flow patterns. Notably, this study innovatively incorporates migration, weather, and epidemic data into machine-learning models for feature extraction. Moreover, it introduces weighted factors—\u0000<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>g</mi>\u0000 <mi>r</mi>\u0000 <mi>o</mi>\u0000 <mi>w</mi>\u0000 <mi>t</mi>\u0000 <mi>h</mi>\u0000 <mo>,</mo>\u0000 <mspace></mspace>\u0000 <mi>b</mi>\u0000 <mi>a</mi>\u0000 <mi>s</mi>\u0000 <mi>e</mi>\u0000 <mo>,</mo>\u0000 <mspace></mspace>\u0000 <mi>w</mi>\u0000 <mi>e</mi>\u0000 <mi>e</mi>\u0000 <mi>k</mi>\u0000 </mrow>\u0000 <annotation>$$ growth, base, week $$</annotation>\u0000 </semantics></math>, and \u0000<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>d</mi>\u0000 <mi>a</mi>\u0000 <mi>y</mi>\u0000 </mrow>\u0000 <annotation>$$ day $$</annotation>\u0000 </semantics></math>—to enhance the accuracy of \u0000<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>C</mi>\u0000 <mi>F</mi>\u0000 <mi>I</mi>\u0000 </mrow>\u0000 <annotation>$$ CFI $$</annotation>\u0000 </semantics></math> prediction. Among the combined models, CCFP outperforms others with remarkable scientific precision (root mean squared error, \u0000<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>R</mi>\u0000 <mi>M</mi>\u0000 <mi>S</mi>\u0000 <mi>E</mi>\u0000 <mo>=</mo>\u0000 <mn>2.04</mn>\u0000 </mrow>\u0000 ","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 2","pages":"730-752"},"PeriodicalIF":3.4,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143117893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper describes the use of cross-learning with panel data modeling for stacking regressions of different predictive models for time series employment across occupations in Europe during the last 15 years. The ARIMA and state space models were used for the predictions on the first-level model ensemble. On the second level, the time series predictions of these models were combined for stacking, using panel data estimators as a cross-learner and also exploiting the strong hierarchical data structure (time series nested in occupational groups). Very few methods adopt stacking to generate ensembles for time series regressions. Indeed, to the best of our knowledge, panel data modeling has never before been used as a cross-learner in staking strategies. Empirical application was used to fit employment by occupations in 30 European countries between 2010 Q1 and 2022 Q4, using the last year as the test set. The empirical results show that using panel data modeling as a multivariate time series cross-learner that stacks univariate time series base models—especially when they do not produce accurate predictions—is an alternative worthy of consideration, also with respect to such classical aggregation schemes as optimal and equal weighting.
{"title":"Cross-Learning With Panel Data Modeling for Stacking and Forecast Time Series Employment in Europe","authors":"Pietro Giorgio Lovaglio","doi":"10.1002/for.3224","DOIUrl":"https://doi.org/10.1002/for.3224","url":null,"abstract":"<p>This paper describes the use of cross-learning with panel data modeling for stacking regressions of different predictive models for time series employment across occupations in Europe during the last 15 years. The ARIMA and state space models were used for the predictions on the first-level model ensemble. On the second level, the time series predictions of these models were combined for stacking, using panel data estimators as a cross-learner and also exploiting the strong hierarchical data structure (time series nested in occupational groups). Very few methods adopt stacking to generate ensembles for time series regressions. Indeed, to the best of our knowledge, panel data modeling has never before been used as a cross-learner in staking strategies. Empirical application was used to fit employment by occupations in 30 European countries between 2010 Q1 and 2022 Q4, using the last year as the test set. The empirical results show that using panel data modeling as a multivariate time series cross-learner that stacks univariate time series base models—especially when they do not produce accurate predictions—is an alternative worthy of consideration, also with respect to such classical aggregation schemes as optimal and equal weighting.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 2","pages":"753-780"},"PeriodicalIF":3.4,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/for.3224","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143117894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We test for state-dependent bias in the European Central Bank's inflation projections. We show that the Eurosystem/European Central Bank (ECB) tends to underpredict when the observed inflation rate at the time of forecasting is higher than an estimated threshold of 1.8%. The bias is most pronounced at intermediate forecasting horizons. This suggests that inflation is projected to revert towards the target too quickly. These results cannot be fully explained by the persistence embedded in the forecasting models or by errors in the exogenous assumptions on interest rates, exchange rates, or oil prices. The state-dependent bias may be consistent with the aim of managing inflation expectations, as published forecasts play a central role in the ECB's monetary policy communication strategy.
{"title":"The Bias of the ECB Inflation Projections: A State-Dependent Analysis","authors":"Eleonora Granziera, Pirkka Jalasjoki, Maritta Paloviita","doi":"10.1002/for.3236","DOIUrl":"https://doi.org/10.1002/for.3236","url":null,"abstract":"<div>\u0000 \u0000 <p>We test for state-dependent bias in the European Central Bank's inflation projections. We show that the Eurosystem/European Central Bank (ECB) tends to underpredict when the observed inflation rate at the time of forecasting is higher than an estimated threshold of 1.8%. The bias is most pronounced at intermediate forecasting horizons. This suggests that inflation is projected to revert towards the target too quickly. These results cannot be fully explained by the persistence embedded in the forecasting models or by errors in the exogenous assumptions on interest rates, exchange rates, or oil prices. The state-dependent bias may be consistent with the aim of managing inflation expectations, as published forecasts play a central role in the ECB's monetary policy communication strategy.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 3","pages":"922-940"},"PeriodicalIF":3.4,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143565407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study investigates the role of media tone on inflation expectations. Examining the relationships between news and the inflation expectations of various US demographic groupings, we find that traditional news influences older cohorts, whereas social media news align more closely with the expectations of younger and more educated groups. Interestingly, social media correspond more closely than traditional news with the expectations of professional forecasters. Our analysis shows that media influences can persist for longer than a year, highlighting the importance of historical inflation data and the gradual adaptation of new information. Additionally, we find that separate media tones for specific news topics such as “Inflation & Fed” and “Healthcare Costs” resonate differently across demographic groups. These insights highlight the nuanced role of media in shaping inflation expectations across demographic segments.
{"title":"Media Tone: The Role of News and Social Media on Heterogeneous Inflation Expectations","authors":"Joni Heikkinen, Kari Heimonen","doi":"10.1002/for.3225","DOIUrl":"https://doi.org/10.1002/for.3225","url":null,"abstract":"<p>This study investigates the role of media tone on inflation expectations. Examining the relationships between news and the inflation expectations of various US demographic groupings, we find that traditional news influences older cohorts, whereas social media news align more closely with the expectations of younger and more educated groups. Interestingly, social media correspond more closely than traditional news with the expectations of professional forecasters. Our analysis shows that media influences can persist for longer than a year, highlighting the importance of historical inflation data and the gradual adaptation of new information. Additionally, we find that separate media tones for specific news topics such as “Inflation & Fed” and “Healthcare Costs” resonate differently across demographic groups. These insights highlight the nuanced role of media in shaping inflation expectations across demographic segments.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 3","pages":"881-921"},"PeriodicalIF":3.4,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/for.3225","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143565253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Different window sizes may produce different empirical results. However, how to choose an ideal window size is still an open question. We investigate how the window size affects the predictive performance of volatility. The empirical results show that the loss function for volatility prediction takes on a U-shape as the window size increases. This suggests that if the window size is chosen too large or too small, the loss function tends to be large and the model's predictive accuracy decreases. A window size of between 1000 and 2000 observations is ideal for various assets because it can produce relatively minimal forecast errors. From an asset allocation perspective, a mean–variance investor can obtain sizeable utility by using a model with a low loss function value for her portfolio. Moreover, the results are robust in a variety of settings.
{"title":"Forecasting Realized Volatility: The Choice of Window Size","authors":"Yuqing Feng, Yaojie Zhang","doi":"10.1002/for.3221","DOIUrl":"https://doi.org/10.1002/for.3221","url":null,"abstract":"<div>\u0000 \u0000 <p>Different window sizes may produce different empirical results. However, how to choose an ideal window size is still an open question. We investigate how the window size affects the predictive performance of volatility. The empirical results show that the loss function for volatility prediction takes on a U-shape as the window size increases. This suggests that if the window size is chosen too large or too small, the loss function tends to be large and the model's predictive accuracy decreases. A window size of between 1000 and 2000 observations is ideal for various assets because it can produce relatively minimal forecast errors. From an asset allocation perspective, a mean–variance investor can obtain sizeable utility by using a model with a low loss function value for her portfolio. Moreover, the results are robust in a variety of settings.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 2","pages":"692-705"},"PeriodicalIF":3.4,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143117165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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