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The effects of governance quality on renewable and nonrenewable energy consumption: An explainable decision frame J. Forecast. (IF 2.627) Pub Date : 2024-03-16 Futian Weng, Dongsheng Cheng, Muni Zhuang, Xin Lu, Cai Yang
This study analyzes the effect of governance quality (six aspects: government effectiveness; control of corruption; voice and accountability; regulatory quality; political stability and absence of violence; and rule of law) on the renewable and nonrenewable energy consumption prediction based on the SHapely Additive exPlanations method for model analysis and interpretability. The empirical findings
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How we missed the inflation surge: An anatomy of post‐2020 inflation forecast errors J. Forecast. (IF 2.627) Pub Date : 2024-03-13 Christoffer Koch, Diaa Noureldin
This paper analyzes the inflation forecast errors over the period 2021Q1–2022Q3 using forecasts of core and headline inflation from the International Monetary Fund World Economic Outlook for a large group of advanced and emerging market economies. The findings reveal evidence of forecast bias that worsened initially and then subsided towards the end of the sample. There is also evidence of forecast
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Sophisticated and small versus simple and sizeable: When does it pay off to introduce drifting coefficients in Bayesian vector autoregressions? J. Forecast. (IF 2.627) Pub Date : 2024-03-12 Martin Feldkircher, Luis Gruber, Florian Huber, Gregor Kastner
We assess the relationship between model size and complexity in the time‐varying parameter vector autoregression (VAR) framework via thorough predictive exercises for the euro area, the United Kingdom, and the United States. It turns out that sophisticated dynamics through drifting coefficients are important in small data sets, while simpler models tend to perform better in sizeable data sets. To combine
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Explainable machine learning techniques based on attention gate recurrent unit and local interpretable model‐agnostic explanations for multivariate wind speed forecasting J. Forecast. (IF 2.627) Pub Date : 2024-03-12 Lu Peng, Sheng‐Xiang Lv, Lin Wang
Wind power has emerged as a successful component within power systems. The ability to reliably and accurately forecast wind speed is of great importance in maintaining the security and stability of the power grid. However, the significance of explaining prediction models has often been overlooked by researchers. To address this gap, this study introduces a novel approach to wind speed forecasting that
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Forecasting the realized volatility of agricultural commodity prices: Does sentiment matter? J. Forecast. (IF 2.627) Pub Date : 2024-03-12 Matteo Bonato, Oguzhan Cepni, Rangan Gupta, Christian Pierdzioch
We analyze the out‐of‐sample predictive power of sentiment for the realized volatility of agricultural commodity price returns. We use high‐frequency intra‐day data covering the period from 2009 to 2020 to estimate realized volatility. Our baseline forecasting model is a heterogeneous autoregressive (HAR) model, which we extend to include sentiment. We further enhance this model by incorporating various
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Well googled is half done: Multimodal forecasting of new fashion product sales with image‐based google trends J. Forecast. (IF 2.627) Pub Date : 2024-03-08 Geri Skenderi, Christian Joppi, Matteo Denitto, Marco Cristani
New fashion product sales forecasting is a challenging problem that involves many business dynamics and cannot be solved by classical forecasting approaches. In this paper, we investigate the effectiveness of systematically probing exogenous knowledge in the form of Google Trends time series and combining it with multi‐modal information related to a brand‐new fashion item, in order to effectively forecast
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Disciplining growth‐at‐risk models with survey of professional forecasters and Bayesian quantile regression J. Forecast. (IF 2.627) Pub Date : 2024-03-08 Milan Szabo
This study presents a novel and fully probabilistic approach for combining model‐based forecasts with surveys or other judgmental forecasts. In our method, survey forecasts are integrated as penalty terms for the model parameters, facilitating a probabilistic exploration of additional insights obtained from surveys. We apply this approach to estimate a growth‐at‐risk model for real GDP growth in the
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An ensemble model for stock index prediction based on media attention and emotional causal inference J. Forecast. (IF 2.627) Pub Date : 2024-03-08 Juanjuan Wang, Shujie Zhou, Wentong Liu, Lin Jiang
Electronic and digital trading models have made stock trading more accessible and convenient, leading to exponential growth in trading data. With a wealth of trading data available, researchers have found opportunities to extract valuable insights by uncovering patterns in stock price movements and market dynamics. Deep learning models are increasingly being employed for stock price prediction. While
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Text‐based corn futures price forecasting using improved neural basis expansion network J. Forecast. (IF 2.627) Pub Date : 2024-03-08 Lin Wang, Wuyue An, Feng‐Ting Li
The accurate forecasting of agricultural futures prices is critical for ensuring national food security. Therefore, this study proposes a text‐based deep learning forecasting model. This model first uses the ChineseBERT + a text convolution neural network to classify Weibo text and obtain a raw sentiment index. Then, complete ensemble empirical mode decomposition with adaptive noise, variational mode
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Can intraday data improve the joint estimation and prediction of risk measures? Evidence from a variety of realized measures J. Forecast. (IF 2.627) Pub Date : 2024-03-08 Zhimin Wu, Guanghui Cai
In recent years, the semiparametric methods for the joint estimation and prediction of value at risk (VaR) and expected shortfall (ES) have triggered great interests and attention. Compared to existing literature which usually incorporates realized volatility (RV) into the dynamic semiparametric risk models, this paper considers three more robust proxies (medRV, BPV, and RK) of intraday volatility
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New runs‐based approach to testing value at risk forecasts J. Forecast. (IF 2.627) Pub Date : 2024-03-08 Marta Małecka
The reformed Basel framework has left value at risk (VaR) as a basic tool of validating risk models. Within this framework, VaR independence tests have been regarded as critical to ensuring stability during periods of financial turmoil. However, until now, there is no consent among researchers regarding the choice of the appropriate test. The available procedures are either inaccurate in finite samples
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Forecasting regional industrial production with novel high‐frequency electricity consumption data J. Forecast. (IF 2.627) Pub Date : 2024-03-06 Robert Lehmann, Sascha Möhrle
In this paper, we study the predictive power of electricity consumption data for regional economic activity. Using unique high‐frequency electricity consumption data from industrial firms for the second‐largest German state, the Free State of Bavaria, we conduct a pseudo out‐of‐sample forecasting experiment for the monthly growth rate of Bavarian industrial production. We find that electricity consumption
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Vine copula‐based scenario tree generation approaches for portfolio optimization J. Forecast. (IF 2.627) Pub Date : 2024-03-06 Xiaolei He, Weiguo Zhang
This paper presents an efficient heuristic to generate multi‐stage scenario trees for portfolio selection problems. In the case of two or more risky assets, investors need to account for the complex multivariate dependence among different assets. The dependence patterns have shown not only asymmetric and fat tails but also time‐varying, and the upper and lower tails have different effect on portfolio
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Correlation‐based tests of predictability J. Forecast. (IF 2.627) Pub Date : 2024-03-06 Pablo Pincheira Brown, Nicolás Hardy
In this paper, we propose a correlation‐based test for the evaluation of two competing forecasts. Under the null hypothesis of equal correlations with the target variable, we derive the asymptotic distribution of our test using the Delta method. This null hypothesis is not necessarily equivalent to the null of equal Mean Squared Prediction Errors (MSPE). Specifically, it might be the case that the
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Electricity price forecasting using quantile regression averaging with nonconvex regularization J. Forecast. (IF 2.627) Pub Date : 2024-03-05 He Jiang, Yao Dong, Jianzhou Wang
Electricity price forecasting (EPF) is an emergent research domain that focuses on forecasting the future electricity market price both deterministically and probabilistically. EPF has attracted enormous interest from both practitioners and scholars since the deregulation of the power market and wide applications of renewable energy sources, such as wind and solar energy. However, forecasting the electricity
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Forecasting elections from partial information using a Bayesian model for a multinomial sequence of data J. Forecast. (IF 2.627) Pub Date : 2024-03-04 Soudeep Deb, Rishideep Roy, Shubhabrata Das
Predicting the winner of an election is of importance to multiple stakeholders. To formulate the problem, we consider an independent sequence of categorical data with a finite number of possible outcomes in each. The data is assumed to be observed in batches, each of which is based on a large number of such trials and can be modeled via multinomial distributions. We postulate that the multinomial probabilities
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Forecasting Consumer Price Index with Federal Open Market Committee Sentiment Index J. Forecast. (IF 2.627) Pub Date : 2024-03-04 Joshua Eklund, Jong‐Min Kim
The Federal Open Market Committee (FOMC) is a component of the Federal Reserve System responsible for overseeing open market operations. The FOMC meets roughly eight or more times per year to assess the economy of the United States. After each meeting, the FOMC releases a statement to the press outlining its assessment of the US economy and its monetary policy stance. The sentiment of these statements
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Issue Information J. Forecast. (IF 2.627) Pub Date : 2024-03-04
No abstract is available for this article.
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Improving demand forecasting for customers with missing downstream data in intermittent demand supply chains with supervised multivariate clustering J. Forecast. (IF 2.627) Pub Date : 2024-02-28 Corey Ducharme, Bruno Agard, Martin Trépanier
In a collaborative supply chain arrangement like vendor-managed inventory, information on product demand at the point of sale is expected to be shared among members of the supply chain. However, in practice, obtaining such information can be costly, and some members may be unwilling or unable to provide the necessary access to the data. As such, large collaborative supply chains with multiple members
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Volatility forecasting for stock market incorporating media reports, investors' sentiment, and attention based on MTGNN model J. Forecast. (IF 2.627) Pub Date : 2024-02-29 Bolin Lei, Yuping Song
In this paper, the self‐monitoring learning model FinBERT is used to identify text emotions, and the sliding time window time‐lagged cross‐correlation (WTLCC) method is utilized to screen Baidu Index keywords for the Shanghai Stock Exchange Index and 18 A‐share listed companies. There are five different types of indicators constructed: news media sentiment, public attention, investor sentiment, investor
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Liquidity‐adjusted value‐at‐risk using extreme value theory and copula approach J. Forecast. (IF 2.627) Pub Date : 2024-02-28 Harish Kamal, Samit Paul
In this study, we propose the application of the GARCH‐EVT‐Copula model in estimating liquidity‐adjusted value‐at‐risk (L‐VaR) of energy stocks while modeling nonlinear dependence between return and bid‐ask spread. Using the L‐VaR framework of Bangia et al. (1998), we present a more parsimonious model that effectively captures non‐zero skewness, excess kurtosis, and volatility clustering of both return
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Forecasting agricultures security indices: Evidence from transformers method J. Forecast. (IF 2.627) Pub Date : 2024-02-28 Ammouri Bilel
In recent years, ensuring food security has become a global concern, necessitating accurate forecasting of agriculture security to aid in policymaking and resource allocation. This article proposes the utilization of transformers, a powerful deep learning technique, for predicting the Agriculture Security Index ( ). The is a comprehensive metric that evaluates the stability and resilience of agricultural
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Post‐COVID inflation dynamics: Higher for longer J. Forecast. (IF 2.627) Pub Date : 2024-02-28 Randal Verbrugge, Saeed Zaman
We implement a novel nonlinear structural model featuring an empirically successful frequency‐dependent and asymmetric Phillips curve; unemployment frequency components interact with three components of core personal consumption expenditures (PCE)—core goods, housing, and core services ex‐housing—and a variable capturing supply shocks. Forecast tests verify accuracy in its unemployment–inflation trade‐offs
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A novel hybrid forecasting model with feature selection and deep learning for wind speed research J. Forecast. (IF 2.627) Pub Date : 2024-02-28 Xuejun Chen, Ying Wang, Haitao Zhang, Jianzhou Wang
Accurate wind speed prediction is of great importance for the operation of wind farms, and extensive efforts have been made to develop effective forecasting methods in this regard. However, the feature selection of data input as well as optimization of deep learning models have received comparatively less attention, leading to unreliable forecasting results. This research proposes a novel hybrid model
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Credit risk prediction based on causal machine learning: Bayesian network learning, default inference, and interpretation J. Forecast. (IF 2.627) Pub Date : 2024-02-27 Jiaming Liu, Xuemei Zhang, Haitao Xiong
The predictive and interpretable power of models is crucial for financial risk management. The purpose of this study was to perform credit risk prediction in a structured causal network with four stages—data processing, structural learning, parameter learning, and interpretation of inferences—and use six real credit datasets to conduct empirical research on the proposed model. Compared with traditional
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A forecasting model for oil prices using a large set of economic indicators J. Forecast. (IF 2.627) Pub Date : 2024-02-27 Jihad El Hokayem, Ibrahim Jamali, Ale Hejase
This paper examines the predictability of the changes in Brent oil futures prices using a multilayer perceptron artificial neural network that exploits the information contained in the largest possible set of economic indicators. Feature engineering is employed to identify the most important predictors of the change in Brent oil futures prices. We find that oil‐market‐specific variables are important
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Applying k‐nearest neighbors to time series forecasting: Two new approaches J. Forecast. (IF 2.627) Pub Date : 2024-02-26 Samya Tajmouati, Bouazza E. L. Wahbi, Adel Bedoui, Abdallah Abarda, Mohamed Dakkon
The k‐nearest neighbors algorithm is one of the prominent techniques used in classification and regression. Despite its simplicity, the k‐nearest neighbors has been successfully applied in time series forecasting. However, the selection of the number of neighbors and feature selection is a daunting task. In this paper, we introduce two methodologies for forecasting time series that we refer to as Classical
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Interpretable corn future price forecasting with multivariate time series J. Forecast. (IF 2.627) Pub Date : 2024-02-26 Binrong Wu, Zhongrui Wang, Lin Wang
Efforts in corn future price forecasting and early warning play a vital role in guiding the high‐quality development of the agricultural economy. However, recent years have witnessed significant fluctuations in global corn future prices due to the impact of COVID‐19 and the escalating risks associated with geopolitical conflicts. Therefore, there is an urgent need for accurate and efficient methods
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Do search queries predict violence against women? A forecasting model based on Google Trends J. Forecast. (IF 2.627) Pub Date : 2024-02-26 Nicolás Gonzálvez‐Gallego, María Concepción Pérez‐Cárceles, Laura Nieto‐Torrejón
This paper introduces a new indicator for reported intimate partner violence against women based on search query time series from Google Trends. This indicator is built up from the relative popularity of three topic‐related keywords. We propose a predictive model based on this specific Google index that is assessed relative to two alternative models: the first one includes the lagged variable, while
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Forecasting realized volatility of crude oil futures prices based on machine learning J. Forecast. (IF 2.627) Pub Date : 2024-02-19 Jiawen Luo, Tony Klein, Thomas Walther, Qiang Ji
Extending the popular HAR model with additional information channels to forecast realized volatility of WTI futures prices, we show that machine learning-generated forecasts provide better forecasting quality and that portfolios that are constructed with these forecasts outperform their competing models resulting in economic gains. Analyzing the selection process, we show that information channels
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International evidence of the forecasting ability of option-implied distributions J. Forecast. (IF 2.627) Pub Date : 2024-02-19 Pedro Serrano, Antoni Vaello-Sebastià, M. Magdalena Vich Llompart
This paper analyzes the forecasting ability of option-implied distributions of 12 stock indexes representative of the most relevant economic regions for a long period ranging from 1996 to 2021. After performing alternative tests, the rejection of the forecasting ability of the risk-neutral densi (RNDs) is not evident, since results are mixed depending on the test performed and market studied: The forecasting
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Forecasting the high-frequency volatility based on the LSTM-HIT model J. Forecast. (IF 2.627) Pub Date : 2024-02-18 Guangying Liu, Ziyan Zhuang, Min Wang
Volatility forecasting from high-frequency data plays a crucial role in many financial fields, such as risk management, option pricing, and portfolio management. Many existing statistical models could better describe and forecast the characteristics of volatility, whereas they do not simultaneously account for the long-term memory of volatility, the nonlinear characteristics of high-frequency data
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Conservatism and information rigidity of the European Bank for Reconstruction and Development's growth forecast: Quarter-century assessment J. Forecast. (IF 2.627) Pub Date : 2024-02-18 Yoichi Tsuchiya
This study assesses the performance of the GDP growth forecasts by the European Bank for Reconstruction and Development for 38 countries between 1994 and 2019. It presents the following results. First, forecast performances improved over time. Second, the projections were mostly conservative, except for some countries with optimistic next-year forecasts. Third, these forecasts were broadly rational
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Tail risk forecasting with semiparametric regression models by incorporating overnight information J. Forecast. (IF 2.627) Pub Date : 2024-02-20 Cathy W. S. Chen, Takaaki Koike, Wei‐Hsuan Shau
This research incorporates realized volatility and overnight information into risk models, wherein the overnight return often contributes significantly to the total return volatility. Extending a semiparametric regression model based on asymmetric Laplace distribution, we propose a family of RES‐CAViaR‐oc models by adding overnight return and realized measures as a nowcasting technique for simultaneously
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Space, mortality, and economic growth J. Forecast. (IF 2.627) Pub Date : 2024-02-14 Kyran Cupido, Petar Jevtić, Tim J. Boonen
Currently, most academic research involving the mortality modeling of multiple populations mainly focuses on factor-based approaches. Increasingly, these models are enriched with socio-economic determinants. Yet these emerging mortality models come with little attention to interpretable spatial model features. Such features could be highly valuable to demographers and old-age benefit providers in need
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Measuring the advantages of contemporaneous aggregation in forecasting J. Forecast. (IF 2.627) Pub Date : 2024-02-14 Zeda Li, William W. S. Wei
Suppose an underlying multivariate time series is contemporaneously aggregated under a known aggregation mechanism, and a lower dimensional multivariate aggregated time series is obtained. To forecast the aggregated time series, one could consider two general strategies: first, aggregate the forecasts of the underlying time series; second, forecast the aggregated time series directly. Intuitively,
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Hybrid convolutional long short-term memory models for sales forecasting in retail J. Forecast. (IF 2.627) Pub Date : 2024-02-13 Thais de Castro Moraes, Xue-Ming Yuan, Ek Peng Chew
This study proposes novel sales forecasting approaches that merge deep learning methods in a hybrid model. Long short-term memory (LSTM) is adopted for modeling the temporal characteristics of the data, whereas the convolutional neural network (CNN) focuses on identifying and extracting relevant exogenous information. We propose stacked (S-CNN-LSTM) and parallel (P-CNN-LSTM) hybrid architectures to
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Forecasting multi-frequency intraday exchange rates using deep learning models J. Forecast. (IF 2.627) Pub Date : 2024-02-15 Muhammad Arslan, Ahmed Imran Hunjra, Wajid Shakeel Ahmed, Younes Ben Zaied
This paper examines the behavior of currencies' intraday exchange rates with mainly focuses on predicting these behaviors through deep learning models. The time series data are used in this study and comprise intraday exchange rate data for seven volatile currencies, recorded at two different frequency intervals: 1 h and 30 min. The data cover the time frame from January 1, 2018, to December 31, 2020
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Using deep (machine) learning to forecast US inflation in the COVID-19 era J. Forecast. (IF 2.627) Pub Date : 2024-02-11 David Stoneman, John V. Duca
The 2021–2022 surge in US inflation was unanticipated by the Survey of Professional Forecasters (SPF) and other macroeconomists and institutions. This study assesses whether nascent deep learning frameworks and methods more accurately project recent core personal consumption expenditures inflation. We create a recurrent neural network (RNN) to forecast long-term inflation, and after training on 60 years
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Modeling the effects of Brexit on the British economy J. Forecast. (IF 2.627) Pub Date : 2024-02-08 Patrick Minford, Zheyi Zhu
We estimate the short run effects of Brexit border disruption on the UK economy. We estimate a structural VAR for the UK, where Brexit effects are identified by the dates of Brexit events, the referendum, and the exit from the single market. We find evidence of short run effects of Brexit: temporary effects on GDP, exports and imports (slightly negative), and on inflation and interest rates (slightly
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Macroeconomic conditions and bank failure J. Forecast. (IF 2.627) Pub Date : 2024-02-04 Qiongbing Wu, Rebel A. Cole
Utilizing a simple time-varying hazard model, we incorporate nationwide and state-level economic variables with banking-industry and bank-level data to examine U.S. bank failures during 1977–2019. We find that bank-level financial conditions are more essential in predicting bank failure, although macro factors affect the failure likelihood of vulnerable banks. We also find that banking-industry market
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Issue Information J. Forecast. (IF 2.627) Pub Date : 2024-02-01
No abstract is available for this article.
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Forecasting in turbulent times J. Forecast. (IF 2.627) Pub Date : 2024-01-30 Nikolaos Giannellis, Stephen G. Hall, Georgios P. Kouretas, George S. Tavlas
Since the beginning of this century, the global economy has been hit by a series of unforeseen shocks, including the Global Financial Crisis, the euro area's sovereign debt crisis, and most recently, the global inflation surge. To motivate this special issue, we provide a brief overview of recent methods that have been proposed to improve the ability of forecast models to predict shocks and to capture
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Forecasting exchange rates: An iterated combination constrained predictor approach J. Forecast. (IF 2.627) Pub Date : 2024-01-30 Antonios K. Alexandridis, Ekaterini Panopoulou, Ioannis Souropanis
Forecasting exchange rate returns is of great interest to both academics and practitioners. In this study, we forecast daily exchange rate returns of six widely traded currencies using combination and dimensionality reduction methods. We propose a hybrid iterated combination with constrained predictor approach. In addition, we examine the impact of positivity constraints on the forecasting ability
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Trust and monetary policy J. Forecast. (IF 2.627) Pub Date : 2024-01-26 Paul De Grauwe, Yuemei Ji
We analyze how trust affects the transmission of negative demand and supply shocks using a behavioral macroeconomic model. We define trust to have two dimensions: trust in the central bank's inflation target and trust in the central bank's capacity to stabilize the business cycle. We find, first, that when large negative shocks occur, the subsequent trajectories taken by output gap and inflation typically
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“Combine to compete: Improving fiscal forecast accuracy over time” J. Forecast. (IF 2.627) Pub Date : 2024-01-21 Laura Carabotta, Peter Claeys
Budget forecasts have become increasingly important as a tool of fiscal management to influence expectations of bond markets and the public at large. Difficulties in projecting macroeconomic variables in volatile economic times—together with political bias—thwart the accuracy of budget forecasts. Pooling information from many different forecasters can still lead to substantial gains in predictive accuracy
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Density forecast combinations: The real-time dimension J. Forecast. (IF 2.627) Pub Date : 2024-01-18 Peter McAdam, Anders Warne
Euro area real-time density forecasts from three dynamic stochastic general equilibrium (DSGE) and three Bayesian vector autoregression (BVAR) models are compared with six combination methods over the sample 2001Q1–2019Q4. The terms information and observation lag are introduced to distinguish time shifts between data vintages and actuals used to compute model weights and compare the forecast, respectively
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The term structure of interest rates and economic activity: Evidence from the COVID-19 pandemic J. Forecast. (IF 2.627) Pub Date : 2024-01-16 Evangelos Salachas, Georgios P. Kouretas, Nikiforos T. Laopodis
This paper tests the accuracy and predictability of two term structure models using both yields-only and factor-augmented specifications focusing on the recent COVID-19 crisis. In addition, we test the predictive ability of the yield curve on economic activity for the United States and other advanced countries. We provide evidence that models with an enhanced information set, including COVID-19 factors
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An evaluation of the inflation forecasting performance of the European Central Bank, the Federal Reserve, and the Bank of England J. Forecast. (IF 2.627) Pub Date : 2024-01-16 Eleni Argiri, Stephen G. Hall, Angeliki Momtsia, Daphne Marina Papadopoulou, Ifigeneia Skotida, George S. Tavlas, Yongli Wang
We provide an overview of the formulation of the forecasts of the European Central Bank, the Federal Reserve, and the Bank of England. We also provide statistical assessments of the performance of the forecasting process of those central banks. We find that the inflation forecasts have, by-and-large, been unbiased and efficient at the very short-term forecast horizon. The performance deteriorates over
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Inflation forecasting with rolling windows: An appraisal J. Forecast. (IF 2.627) Pub Date : 2024-01-14 Stephen G. Hall, George S. Tavlas, Yongli Wang, Deborah Gefang
We examine the performance of rolling windows procedures in forecasting inflation. We implement rolling windows augmented Dickey–Fuller (ADF) tests and then conduct a set of Monte Carlo experiments under stylized forms of structural breaks. We find that as long as the nature of inflation is either stationary or non-stationary, popular varying-length window techniques provide little advantage in forecasting
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Gated recurrent unit network: A promising approach to corporate default prediction J. Forecast. (IF 2.627) Pub Date : 2024-01-12 Michał Thor, Łukasz Postek
This paper presents a promising approach using gated recurrent unit (GRU) network to predict bankruptcy based on the whole sequence of financial statements of the companies listed on an unregulated market. This approach contrasts with the traditional literature where default prediction is usually tackled with methods that do not fully account for a company's history. The GRU network can be used to
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Forecasting food price inflation during global crises J. Forecast. (IF 2.627) Pub Date : 2024-01-17 Patricia Toledo, Roberto Duncan
In this paper, we consider the forecasting of domestic food price inflation (DFPI) using global indicators, with emphasis on episodes of macroeconomic turbulence, namely, the Global Financial Crisis (GFC) and the COVID-19 pandemic and its subsequent repercussions. Our monthly dataset covers about two decades for more than a hundred economies. We employ dynamic model averaging (DMA) to tackle both model
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Forecasting the containerized freight index with AIS data: A novel information combination method based on gray incidence analysis J. Forecast. (IF 2.627) Pub Date : 2024-01-03 Yanhui Chen, Ailing Feng, Shun Chen, Jackson Jinhong Mi
This paper uses the container shipping capacities of 11 major trade lanes, obtained from automatic identification system (AIS), to construct a common factor based on gray incidence analysis (GIA) in the aim of improving the predictability of containerized freight index. Our results show that the common factor generated by GIA consistently exhibits better out-of-sample prediction performances than principal
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Forecasts with Bayesian vector autoregressions under real time conditions J. Forecast. (IF 2.627) Pub Date : 2023-12-28 Michael Pfarrhofer
This paper investigates the sensitivity of forecast performance metrics to taking a real time versus pseudo out-of-sample perspective. I use monthly vintages of two popular datasets for the United States and the euro area. Variants of vector autoregressions, varying the size of the information sets and the conditional mean and variance specification, are considered. The results suggest differences
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Empirical prediction intervals for additive Holt–Winters methods under misspecification J. Forecast. (IF 2.627) Pub Date : 2023-12-13 Boning Yang, Xinyi Tang, Chun Yip Yau
Holt–Winters (HW) methods have been widely used by practitioners for the prediction of time series. However, traditional prediction intervals associated with the HW methods are only theoretically justified for a few types of SARIMA processes. In this article, we propose an empirical prediction interval for a general class of prediction procedures containing the HW methods as special cases. We establish
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Forecasting CPI with multisource data: The value of media and internet information J. Forecast. (IF 2.627) Pub Date : 2023-12-05 Tingguo Zheng, Xinyue Fan, Wei Jin, Kuangnan Fang
Using a large Chinese news corpus and Internet search data, we conduct an in-depth out-of-sample forecasting study of Consumer Price Index (CPI) with the monthly macroeconomic database. For this purpose, we combine penalized regression and mixed-frequency data sampling (MIDAS) methods to deal with the mixed-frequency and high-dimensional problems. Then we measure the time-varying contributions of data
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Issue Information J. Forecast. (IF 2.627) Pub Date : 2023-12-03
No abstract is available for this article.
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Downturns and changes in the yield slope J. Forecast. (IF 2.627) Pub Date : 2023-12-01 Mirko Abbritti, Juan Equiza, Antonio Moreno, Tommaso Trani
We show that the slope of the sovereign yield curve predicts future economic activity not only through its level but also through its changes (or differences) over time. Our results with US data show that the additional inclusion of the changes of the yield slope in the traditional regressions significantly increases the explanatory power of the yield curve. Through the yield slope decomposition, we
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RMB exchange rate forecasting using machine learning methods: Can multimodel select powerful predictors? J. Forecast. (IF 2.627) Pub Date : 2023-11-30 Xing Yu, Yanyan Li, Xinxin Wang
This paper aims to study the phased influencing factors of renminbi (RMB) exchange rate (CNY against USD) and investigate the predictability of the factors selected by multimodel. We first take the time points when China's main exchange reform policies are launched as the demarcation points and divide the entire sample from July 2005 to December 2020 into three periods. Then, we select the potential
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Forecasting air passenger travel: A case study of Norwegian aviation industry J. Forecast. (IF 2.627) Pub Date : 2023-11-30 Angesh Anupam, Isah A. Lawal
Accurate forecasting of airline passenger traffic is important for facilitating the effective management and planning of aviation resources. In this study, we explore the air passenger traffic in the Norwegian aviation industry by collecting the passenger flow data and the corresponding measurements of the weather conditions affecting the flow from the different airports in Norway. We then proposed