Journal of Forecasting最新文献_第3页

Taming Data-Driven Probability Distributions 驯服数据驱动的概率分布

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-11-19 DOI: 10.1002/for.3208

Jozef Baruník, Luboš Hanus

引用次数: 0

Ensemble Multitask Prediction of Air Pollutants Time Series: Based on Variational Inference, Data Projection, and Generative Adversarial Network 空气污染物时间序列的集成多任务预测：基于变分推理、数据投影和生成对抗网络

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-11-18 DOI: 10.1002/for.3218

Kang Wang, Chao Qu, Jianzhou Wang, Zhiwu Li, Haiyan Lu

{"title":"Ensemble Multitask Prediction of Air Pollutants Time Series: Based on Variational Inference, Data Projection, and Generative Adversarial Network","authors":"Kang Wang, Chao Qu, Jianzhou Wang, Zhiwu Li, Haiyan Lu","doi":"10.1002/for.3218","DOIUrl":"https://doi.org/10.1002/for.3218","url":null,"abstract":"<div>\u0000 \u0000 <p>In light of the mounting environmental pressures, especially the significant threat urban air pollution poses to public health, there arises an imperative need to develop a data-driven model for air pollution prediction. However, contemporary deep learning techniques, such as recurrent neural networks, often struggle to effectively capture the underlying data patterns and distributions, resulting in reduced model stability. To address this gap, this study introduces an ensemble Wasserstein generative adversarial network framework (EWGF) to enhance the stability and accuracy of PM<sub>2.5</sub> predictions by facilitating the acquisition of more informative data representations through Wasserstein generative adversarial network. The framework contains an intricate feature extraction pipeline that automatically learns features containing residual information as representations of potential features, effectively ameliorating the underutilization of feature information. We address a nonconvex multi-objective optimization problem associated with amalgamating diverse Wasserstein generative adversarial network architectures, which enhance the inherent instability of the predictions. Furthermore, an adaptive search strategy is introduced to ascertain the optimal distribution of prediction residuals, thereby expanding the prediction interval estimation method based on residual distribution. We rigorously evaluate the proposed framework using datasets from three major Indian cities, and our experiments unequivocally show that the EWGF outperforms existing solutions in both PM<sub>2.5</sub> point prediction and interval prediction, evidenced by an approximate 8.07% reduction in mean absolute percentage error and an approximate 19.41% improvement in prediction interval score compared to the baseline model.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 2","pages":"646-675"},"PeriodicalIF":3.4,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143116411","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}

引用次数: 0

Vector SHAP Values for Machine Learning Time Series Forecasting 用于机器学习时间序列预测的矢量SHAP值

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-11-18 DOI: 10.1002/for.3220

Ji Eun Choi, Ji Won Shin, Dong Wan Shin

{"title":"Vector SHAP Values for Machine Learning Time Series Forecasting","authors":"Ji Eun Choi, Ji Won Shin, Dong Wan Shin","doi":"10.1002/for.3220","DOIUrl":"https://doi.org/10.1002/for.3220","url":null,"abstract":"<div>\u0000 \u0000 <p>We propose a new vector SHapley Additive exPlanations (SHAP) to interpret machine learning models for forecasting time series using lags of predictor variables. Unlike the standard SHAP measuring the contribution of each lag of each predictor variable, the proposed vector SHAP measures the contribution of the vector of the lags of each variable. The vector SHAP has an advantage of faster computation over the standard SHAP. Some desirable properties of the vector SHAP (vector local accuracy, vector missingness, and vector consistency) are established. A Monte Carlo simulation shows that the vector SHAP has a much faster computing time than the SHAP; the difference of the standard SHAP and the vector SHAP is small; the sampling SHAP is sensitive to the sampling proportion in a range of practical application; the vector SHAP mitigates the sensitivity issue. The vector SHAP is applied to the realized volatility of world major stock price indices of 16 countries for forecasting the realized volatility of South Korea stock price index, KOSPI. Further vectoring by regions of Europe, North America, and Asia yields vector SHAP value for each region which is very close to the sum of vector SHAP values of the countries of the region, illustrating usefulness of the strategy of vectoring.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 2","pages":"635-645"},"PeriodicalIF":3.4,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143116444","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}

引用次数: 0

Time-Varying US Government Spending Anticipation in Real Time 实时变化的美国政府支出预期

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-11-18 DOI: 10.1002/for.3234

Pascal Goemans, Robinson Kruse-Becher

{"title":"Time-Varying US Government Spending Anticipation in Real Time","authors":"Pascal Goemans, Robinson Kruse-Becher","doi":"10.1002/for.3234","DOIUrl":"https://doi.org/10.1002/for.3234","url":null,"abstract":"<p>Due to legislation and implementation lags, forward-looking economic agents anticipate changes in fiscal policy variables before they actually occur. The literature shows that this foresight poses a challenge to the econometric analysis of fiscal policies. While most of the literature uses fully revised data to investigate the degree of fiscal foresight, we use forecasts from the Survey of Professional Forecasters (SPF), the Greenbook/Tealbook from the Federal Reserve, and the Real-Time Data Set for Macroeconomists. Furthermore, we distinguish between federal as well as state and local consumption & investment expenditures. We find that real-time data matter. Using the first release, the SPF nowcast was able to predict 43% of the out-of-sample fluctuation in federal government spending growth (only 24% using the most recent release). Moreover, the SPF was able to predict 60% and 52% of the cumulated growth in federal and state & local government spending growth over a 1-year horizon. We use the Diebold–Mariano tests and model confidence sets to investigate whether SPF forecasts significantly outperform the Greenbook projections and forecasts from purely backward-looking time series models. Compared to the SPF and Greenbook projections, the time series models perform inferior at most forecast horizons. In addition, so-called information advantage regressions reveal that most forecasts could be improved by using the information of the SPF. Using rolling windows, we document remarkable time-variation in the degree of fiscal foresight of the SPF and its information advantage against (augmented) autoregressive models and the Greenbook. Particularly during the 1980s and 2000s, we find a strong degree of anticipation for government spending at the federal level by the SPF and the central bank.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 3","pages":"867-880"},"PeriodicalIF":3.4,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/for.3234","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143565423","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}

引用次数: 0

Sectoral Corporate Profits and Long-Run Stock Return Volatility in the United States: A GARCH-MIDAS Approach 美国部门企业利润与长期股票回报波动：GARCH-MIDAS方法

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-11-16 DOI: 10.1002/for.3207

Afees Salisu, Kazeem O. Isah, Ahamuefula Ephraim Ogbonna

引用次数: 0

Predictive Power of Key Financial Variables During the Unconventional Monetary Policy Era 非常规货币政策时代关键金融变量的预测能力

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-11-15 DOI: 10.1002/for.3233

Petri Kuosmanen, Juuso Vataja

{"title":"Predictive Power of Key Financial Variables During the Unconventional Monetary Policy Era","authors":"Petri Kuosmanen, Juuso Vataja","doi":"10.1002/for.3233","DOIUrl":"https://doi.org/10.1002/for.3233","url":null,"abstract":"<p>This study investigates the forecasting power of three well-established financial predictors during the prolonged era of unconventional monetary policy: the term spread, the short-term interest rate, and stock returns. The focus is on predicting GDP growth in both the United States and the Euro area. Our out-of-sample forecasting analysis specifically targets the period characterized by the short-term interest rate effectively bounded at or near the zero lower bound. We recognize that the information content of the term spread is likely to change under such circumstances. Similarly, the dynamics of the short-term interest rate could be altered due to unconventional monetary policy measures. To address this, we modify the short rate calculation by incorporating the shadow interest. This shadow interest rate can go much lower on the negative side than normal interest rates, making it a potentially more accurate rate to describe the monetary policy stance of central banks. The forecasting analysis covers the period from 2009:1 to 2022:3. Our results unambiguously reveal that the predictive power of the term spread completely vanishes during the zero lower bound era. Although the shadow rate has minor predictive content, the strongest predictor consistently lies in real stock returns during unconventional monetary policy. Our findings challenge the conventional wisdom and the stylized fact of the term spread as the most reliable financial predictor for economic activity. According to our results, this does not hold true under unconventional monetary policy, and using the shadow interest rate does not make a major difference in that respect. By shedding light on the changing dynamics during unconventional monetary policy, our study contributes novel insights to the existing literature.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 3","pages":"856-866"},"PeriodicalIF":3.4,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/for.3233","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143565252","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}

引用次数: 0

Modelling and Forecasting of Exchange Rate Pairs Using the Kalman Filter 基于卡尔曼滤波的汇率对建模与预测

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-11-15 DOI: 10.1002/for.3217

Paresh Date, Janeeta Maunthrooa

{"title":"Modelling and Forecasting of Exchange Rate Pairs Using the Kalman Filter","authors":"Paresh Date, Janeeta Maunthrooa","doi":"10.1002/for.3217","DOIUrl":"https://doi.org/10.1002/for.3217","url":null,"abstract":"<p>Developing and employing practically useful and easy to calibrate models for prediction of exchange rates remains a challenging task, especially for highly volatile emerging market currencies. In this paper, we propose a novel approach for joint prediction of correlated exchange rates for two different currencies with respect to the same base currency. For this purpose, we reformulate a generalized version of a bivariate ARMA model into a state space model and use the Kalman filter for estimation and forecasting of the underlying exchange rates as latent variables. With extensive numerical experiments spanning 18 different exchange rates (across both emerging markets, developing and developed economies), we demonstrate that our approach consistently outperforms univariate ARMA models as well as the random walk model in short term out-of-sample prediction for various exchange rate pairs. Our study fills a gap in the empirical finance literature in terms of robust, explainable, accurate, and easy to calibrate models for forecasting correlated exchange rates. The proposed methodology has applications in exchange rate risk management as well as pricing of financial derivatives based on two exchange rates.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 2","pages":"606-622"},"PeriodicalIF":3.4,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/for.3217","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143115547","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}

引用次数: 0

Parametric Quantile Autoregressive Conditional Duration Models With Application to Intraday Value-at-Risk Forecasting 参数分位数自回归条件持续时间模型及其在日内风险值预测中的应用

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-11-13 DOI: 10.1002/for.3214

Helton Saulo, Suvra Pal, Rubens Souza, Roberto Vila, Alan Dasilva

{"title":"Parametric Quantile Autoregressive Conditional Duration Models With Application to Intraday Value-at-Risk Forecasting","authors":"Helton Saulo, Suvra Pal, Rubens Souza, Roberto Vila, Alan Dasilva","doi":"10.1002/for.3214","DOIUrl":"https://doi.org/10.1002/for.3214","url":null,"abstract":"<div>\u0000 \u0000 <p>The modeling of high-frequency data that qualify financial asset transactions has been an area of relevant interest among statisticians and econometricians—above all, the analysis of time series of financial durations. Autoregressive conditional duration (ACD) models have been the main tool for modeling financial transaction data, where duration is usually defined as the time interval between two successive events. These models are usually specified in terms of a time-varying mean (or median) conditional duration. In this paper, a new extension of ACD models is proposed which is built on the basis of log-symmetric distributions reparametrized by their quantile. The proposed quantile log-symmetric conditional duration autoregressive model allows us to model different percentiles instead of the traditionally used conditional mean (or median) duration. We carry out an in-depth study of theoretical properties and practical issues, such as parameter estimation using maximum likelihood method and diagnostic analysis based on residuals. A detailed Monte Carlo simulation study is also carried out to evaluate the performance of the proposed models and estimation method in retrieving the true parameter values as well as to evaluate a form of residuals. Finally, we derive a semiparametric intraday value-at-risk (IVaR) model and then the proposed models are applied to two price duration data sets.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 2","pages":"589-605"},"PeriodicalIF":3.4,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143114942","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}

引用次数: 0

Temporal Patterns in Migration Flows Evidence from South Sudan 来自南苏丹的移民流动的时间模式证据

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-11-11 DOI: 10.1002/for.3209

Thomas Schincariol, Thomas Chadefaux

引用次数: 0

Forecasting Chinese Stock Market Volatility With Volatilities in Bond Markets 用债券市场波动预测中国股市波动

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-11-10 DOI: 10.1002/for.3215

Likun Lei, Mengxi He, Yi Zhang, Yaojie Zhang

引用次数: 0