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Factor-augmented forecasting in big data International Journal of Forecasting (IF 7.022) Pub Date : 2024-03-16 Juhee Bae
This paper evaluates the predictive performance of various factor estimation methods in big data. Extensive forecasting experiments are examined using seven factor estimation methods with 13 decision rules determining the number of factors. The out-of-sample forecasting results show that the first Partial Least Squares factor (1-PLS) tends to be the best-performing method among all the possible alternatives
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Improving forecasts for heterogeneous time series by “averaging”, with application to food demand forecasts International Journal of Forecasting (IF 7.022) Pub Date : 2024-03-14 Lukas Neubauer, Peter Filzmoser
A common forecasting setting in real-world applications considers a set of possibly heterogeneous time series of the same domain. Due to the different properties of each time series, such as length, obtaining forecasts for each individual time series in a straightforward way is challenging. This paper proposes a general framework utilizing a similarity measure in dynamic time warping to find similar
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Dynamic prediction of the National Hockey League draft with rank-ordered logit models International Journal of Forecasting (IF 7.022) Pub Date : 2024-02-29 Brendan Kumagai, Ryker Moreau, Kimberly Kroetch, Tim B. Swartz
The National Hockey League (NHL) Entry Draft has been an active area of research in hockey analytics over the past decade. Prior research has explored predictive modelling for draft results using player information and statistics as well as ranking data from draft experts. In this paper, we develop a new modelling framework for this problem using a Bayesian rank-ordered logit model based on draft ranking
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The Short-Term Predictability of Returns in Order Book Markets: A Deep Learning Perspective International Journal of Forecasting (IF 7.022) Pub Date : 2024-02-27 Lorenzo Lucchese, Mikko S. Pakkanen, Almut E.D. Veraart
This paper uses deep learning techniques to conduct a systematic large-scale analysis of order book-driven predictability in high-frequency returns. First, we introduce a new and robust representation of the order book, the volume representation. Next, we conduct an extensive empirical experiment to address various questions regarding predictability. We investigate if and how far ahead there is predictability
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Multivariate probabilistic CRPS learning with an application to day-ahead electricity prices International Journal of Forecasting (IF 7.022) Pub Date : 2024-02-14 Jonathan Berrisch, Florian Ziel
This paper presents a new method for combining (or aggregating or ensembling) multivariate probabilistic forecasts, considering dependencies between quantiles and marginals through a smoothing procedure that allows for online learning. We discuss two smoothing methods: dimensionality reduction using Basis matrices and penalized smoothing. The new online learning algorithm generalizes the standard CRPS
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A Bayesian Dirichlet auto-regressive moving average model for forecasting lead times International Journal of Forecasting (IF 7.022) Pub Date : 2024-02-09 Harrison Katz, Kai Thomas Brusch, Robert E. Weiss
In the hospitality industry, lead time data are a form of compositional data that are crucial for business planning, resource allocation, and staffing. Hospitality businesses accrue fees daily, but recognition of these fees is often deferred. This paper presents a novel class of Bayesian time series models, the Bayesian Dirichlet auto-regressive moving average (B-DARMA) model, designed specifically
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Forecasting UK inflation bottom up International Journal of Forecasting (IF 7.022) Pub Date : 2024-02-05 Andreas Joseph, Galina Potjagailo, Chiranjit Chakraborty, George Kapetanios
We forecast CPI inflation indicators in the United Kingdom using a large set of monthly disaggregated CPI item series covering a sample period of twenty years, and employing a range of forecasting tools to deal with the high dimension of the set of predictors. Although an autoregressive model proofs hard to outperform overall, Ridge regression combined with CPI item series performs strongly in forecasting
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Survey density forecast comparison in small samples International Journal of Forecasting (IF 7.022) Pub Date : 2024-01-25 Laura Coroneo, Fabrizio Iacone, Fabio Profumo
We apply fixed-b and fixed-m asymptotics to tests of equal predictive accuracy and of encompassing for survey density forecasts. We verify in an original Monte Carlo design that fixed-smoothing asymptotics delivers correctly sized tests in this framework, even when only a small number of out of sample observations is available. We use the proposed density forecast comparison tests with fixed-smoothing
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Instance-based meta-learning for conditionally dependent univariate multi-step forecasting International Journal of Forecasting (IF 7.022) Pub Date : 2024-01-25 Vitor Cerqueira, Luis Torgo, Gianluca Bontempi
Multi-step prediction is a key challenge in univariate forecasting. However, forecasting accuracy decreases as predictions are made further into the future. This is caused by the decreasing predictability and the error propagation along the horizon. In this paper, we propose a novel method called Forecasted Trajectory Neighbors (FTN) for multi-step forecasting with univariate time series. FTN is a
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Network log-ARCH models for forecasting stock market volatility International Journal of Forecasting (IF 7.022) Pub Date : 2024-01-25 Raffaele Mattera, Philipp Otto
This paper presents a dynamic network autoregressive conditional heteroscedasticity (ARCH) model suitable for high-dimensional cases where multivariate ARCH models are typically no longer applicable. We adopt the theoretical foundations from spatiotemporal statistics and transfer the dynamic ARCH model processes to networks. The model integrates temporally lagged volatility and information from adjacent
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Guerard John B., Macmillan Palgrave, The leading economic indicators and business cycles in the United States: 100 years of empirical evidence and the opportunities for the future (2022), 650 pp., ISBN 978-3-030-99417-4, Hardcover book, USD $79.99 International Journal of Forecasting (IF 7.022) Pub Date : 2024-01-22 Kajal Lahiri
Abstract not available
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Crowd prediction systems: Markets, polls, and elite forecasters International Journal of Forecasting (IF 7.022) Pub Date : 2024-01-22 Pavel Atanasov, Jens Witkowski, Barbara Mellers, Philip Tetlock
What systems should we use to elicit and aggregate judgmental forecasts? Who should be asked to make such forecasts? We address these questions by assessing two widely used crowd prediction systems: prediction markets and prediction polls. Our main test compares a prediction market against team-based prediction polls, using data from a large, multi-year forecasting competition. Each of these two systems
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CRPS-based online learning for nonlinear probabilistic forecast combination International Journal of Forecasting (IF 7.022) Pub Date : 2024-01-20 Dennis van der Meer, Pierre Pinson, Simon Camal, Georges Kariniotakis
Forecast combination improves upon the component forecasts. Most often, combination approaches are restricted to the linear setting only. However, theory shows that if the component forecasts are neutrally dispersed—a requirement for probabilistic calibration—linear forecast combination will only increase dispersion and thus lead to miscalibration. Furthermore, the accuracy of the component forecasts
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Forecasting seasonal demand for retail: A Fourier time-varying grey model International Journal of Forecasting (IF 7.022) Pub Date : 2024-01-18 Lili Ye, Naiming Xie, John E. Boylan, Zhongju Shang
Seasonal demand forecasting is critical for effective supply chain management. However, conventional forecasting methods face difficulties accurately estimating seasonal variations, owing to time-varying demand trends and limited data availability. In this paper, we propose a Fourier time-varying grey model (FTGM) to tackle this issue. The FTGM builds upon grey models, which are effective with limited
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Editorial: Innovations in hierarchical forecasting International Journal of Forecasting (IF 7.022) Pub Date : 2024-01-22 George Athanasopoulos, Rob J. Hyndman, Nikolaos Kourentzes, Anastasios Panagiotelis
Abstract not available
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Properties of the reconciled distributions for Gaussian and count forecasts International Journal of Forecasting (IF 7.022) Pub Date : 2024-01-12 Lorenzo Zambon, Arianna Agosto, Paolo Giudici, Giorgio Corani
Reconciliation enforces coherence between hierarchical forecasts, in order to satisfy a set of linear constraints. While most works focus on the reconciliation of point forecasts, we consider probabilistic reconciliation and we analyze the properties of distributions reconciled via conditioning. We provide a formal analysis of the variance of the reconciled distribution, treating the case of Gaussian
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Acknowledgement to reviewers International Journal of Forecasting (IF 7.022) Pub Date : 2024-01-08
Abstract not available
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Forecasting day-ahead expected shortfall on the EUR/USD exchange rate: The (I)relevance of implied volatility International Journal of Forecasting (IF 7.022) Pub Date : 2024-01-04 Štefan Lyócsa, Tomáš Plíhal, Tomáš Výrost
The existing literature provides mixed results on the usefulness of implied volatility for managing risky assets, while evidence for expected shortfall predictions is almost nonexistent. Given its forward-looking nature, implied volatility might be more valuable than backward-looking measures of realized price fluctuations. Conversely, the volatility risk premium embedded in implied volatility leads
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A probabilistic forecast methodology for volatile electricity prices in the Australian National Electricity Market International Journal of Forecasting (IF 7.022) Pub Date : 2024-01-03 Cameron Cornell, Nam Trong Dinh, S. Ali Pourmousavi
The South Australia region of the Australian National Electricity Market (NEM) displays some of the highest levels of price volatility observed in modern electricity markets. This paper outlines an approach to probabilistic forecasting under these extreme conditions, including spike filtration and several post-processing steps. We propose using quantile regression as an ensemble tool for probabilistic
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Generalized Poisson difference autoregressive processes International Journal of Forecasting (IF 7.022) Pub Date : 2023-12-28 Giulia Carallo, Roberto Casarin, Christian P. Robert
This paper introduces a novel stochastic process with signed integer values. Its autoregressive dynamics effectively captures persistence in conditional moments, rendering it a valuable feature for forecasting applications. The increments follow a Generalized Poisson distribution, capable of accommodating over- and under-dispersion in the conditional distribution, thereby extending standard Poisson
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Forecast reconciliation: A review International Journal of Forecasting (IF 7.022) Pub Date : 2023-12-29 George Athanasopoulos, Rob J. Hyndman, Nikolaos Kourentzes, Anastasios Panagiotelis
Collections of time series formed via aggregation are prevalent in many fields. These are commonly referred to as hierarchical time series and may be constructed cross-sectionally across different variables, temporally by aggregating a single series at different frequencies, or even generalised beyond aggregation as time series that respect linear constraints. When forecasting such time series, a desirable
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Forecasting emergency department occupancy with advanced machine learning models and multivariable input International Journal of Forecasting (IF 7.022) Pub Date : 2023-12-27 Jalmari Tuominen, Eetu Pulkkinen, Jaakko Peltonen, Juho Kanniainen, Niku Oksala, Ari Palomäki, Antti Roine
Emergency department (ED) crowding is a significant threat to patient safety and it has been repeatedly associated with increased mortality. Forecasting future service demand has the potential to improve patient outcomes. Despite active research on the subject, proposed forecasting models have become outdated, due to the quick influx of advanced machine learning models and because the amount of multivariable
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An assessment of the marginal predictive content of economic uncertainty indexes and business conditions predictors International Journal of Forecasting (IF 7.022) Pub Date : 2023-12-22 Yang Liu, Norman R. Swanson
In this paper, we evaluate the marginal predictive content of a variety of new business conditions (BC) predictors as well as nine economic uncertainty indexes (EUIs) constructed using these predictors. Our predictors are defined as observable variables and latent factors extracted from a high-dimensional macroeconomic dataset, and our EUIs are functions of predictive errors from models that incorporate
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Investigating laypeople’s short- and long-term forecasts of COVID-19 infection cycles International Journal of Forecasting (IF 7.022) Pub Date : 2023-12-15 Moon Su Koo, Yun Shin Lee, Matthias Seifert
How do laypeople anticipate the severity of the COVID-19 pandemic in the short and long term? The evolution of COVID-19 infection cases is characterized by wave-shaped cycles, and we examine how individuals make forecasts for this type of time series. Over 42 weeks, we ran forecasting experiments and elicited weekly judgments from the general public to analyze their forecasting behavior (Study 1).
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Nowcasting with panels and alternative data: The OECD weekly tracker International Journal of Forecasting (IF 7.022) Pub Date : 2023-12-14 Nicolas Woloszko
Alternative data are timely but messy. They can provide policymakers with real-time information, but their use is constrained by the complexity of their relationship with official statistics. Data from credit card transactions, search engines, or traffic have been made available since only recently, which makes it more difficult to precisely gauge their relationship with national accounts. This paper
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Lantz Brett, Machine Learning with R: Expert techniques for predictive modelling, 3rd Edition, Packt Publishing Ltd, Birmingham, United Kingdom (2019), 458 pp. £57.56. ISBN: 9781788295864, 1788295862 International Journal of Forecasting (IF 7.022) Pub Date : 2023-12-11 Gnanadarsha Sanjaya Dissanayake
Abstract not available
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Do professional forecasters believe in the Phillips curve? International Journal of Forecasting (IF 7.022) Pub Date : 2023-12-08 Michael P. Clements
The expectations-augmented Phillips curve (PC) is a cornerstone of many macroeconomic models. We consider the extent to which professional forecasters’ inflation and unemployment rate forecasts are ‘theory consistent’, and find much heterogeneity. Perceptions about the responsiveness of inflation to the unemployment rate are shown to depend on whether the respondent was active earlier or later during
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Reservoir computing for macroeconomic forecasting with mixed-frequency data International Journal of Forecasting (IF 7.022) Pub Date : 2023-12-07 Giovanni Ballarin, Petros Dellaportas, Lyudmila Grigoryeva, Marcel Hirt, Sophie van Huellen, Juan-Pablo Ortega
Macroeconomic forecasting has recently started embracing techniques that can deal with large-scale datasets and series with unequal release periods. Mixed-data sampling (MIDAS) and dynamic factor models (DFMs) are the two main state-of-the-art approaches to modeling series with non-homogeneous frequencies. We introduce a new framework, called the multi-frequency echo state network (MFESN), based on
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Forecasting day-ahead electricity prices with spatial dependence International Journal of Forecasting (IF 7.022) Pub Date : 2023-11-29 Yifan Yang, Ju’e Guo, Yi Li, Jiandong Zhou
Market integration connects multiple autarkic electricity markets and facilitates the flow of power across areas. More often than not, market integration can increase social welfare for the whole power system with a clear spatial dependence structure among area electricity prices, which motivates a new perspective on electricity price forecasting. In this paper, we construct a model to forecast the
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Editorial Board International Journal of Forecasting (IF 7.022) Pub Date : 2023-11-22
Abstract not available
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Nowcasting U.S. state-level CO2 emissions and energy consumption International Journal of Forecasting (IF 7.022) Pub Date : 2023-11-18 Jack Fosten, Shaoni Nandi
This paper proposes panel nowcasting methods to obtain timely predictions of CO2 emissions and energy consumption growth across all U.S. states. This is crucial, not least because of the increasing role of sub-national carbon abatement policies but also due to the very delayed publication of the data. Since the state-level CO2 data are constructed from energy consumption data, we propose a new panel
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Rating players by Laplace’s approximation and dynamic modeling International Journal of Forecasting (IF 7.022) Pub Date : 2023-11-10 Hsuan-Fu Hua, Ching-Ju Chang, Tse-Ching Lin, Ruby Chiu-Hsing Weng
The Elo rating system is a simple and widely used method for calculating players’ skills from paired comparison data. Many have extended it in various ways. Yet the question of updating players’ variances remains to be further explored. In this paper, we address the issue of variance update by using the Laplace approximation for posterior distributions, together with a random walk model for the dynamics
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Portfolio selection under non-gaussianity and systemic risk: A machine learning based forecasting approach International Journal of Forecasting (IF 7.022) Pub Date : 2023-11-10 Weidong Lin, Abderrahim Taamouti
The Sharpe-ratio-maximizing portfolio becomes questionable under non-Gaussian returns, and it rules out, by construction, systemic risk, which can negatively affect its out-of-sample performance. In the present work, we develop a new performance ratio that simultaneously addresses these two problems when building optimal portfolios. To robustify the portfolio optimization and better represent extreme
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Cross-temporal probabilistic forecast reconciliation: Methodological and practical issues International Journal of Forecasting (IF 7.022) Pub Date : 2023-11-07 Daniele Girolimetto, George Athanasopoulos, Tommaso Di Fonzo, Rob J. Hyndman
Forecast reconciliation is a post-forecasting process that involves transforming a set of incoherent forecasts into coherent forecasts which satisfy a given set of linear constraints for a multivariate time series. In this paper, we extend the current state-of-the-art cross-sectional probabilistic forecast reconciliation approach to encompass a cross-temporal framework, where temporal constraints are
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Obituary: J. Scott Armstrong International Journal of Forecasting (IF 7.022) Pub Date : 2023-11-07 Fred Collopy, Robert Fildes
Abstract not available
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Evaluating probabilistic classifiers: The triptych International Journal of Forecasting (IF 7.022) Pub Date : 2023-11-04 Timo Dimitriadis, Tilmann Gneiting, Alexander I. Jordan, Peter Vogel
Probability forecasts for binary outcomes, often referred to as probabilistic classifiers or confidence scores, are ubiquitous in science and society, and methods for evaluating and comparing them are in great demand. We propose and study a triptych of diagnostic graphics focusing on distinct and complementary aspects of forecast performance: Reliability curves address calibration, receiver operating
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Short-term stock price trend prediction with imaging high frequency limit order book data International Journal of Forecasting (IF 7.022) Pub Date : 2023-11-03 Wuyi Ye, Jinting Yang, Pengzhan Chen
Predicting price movements over a short period is a challenging problem in high-frequency trading. Deep learning methods have recently been used to forecast short-term prices via limit order book (LOB) data. In this paper, we propose a framework to convert LOB data into a series of standard images in 2D matrices and predict the mid-price movements via an image-based convolutional neural network (CNN)
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DeepTVAR: Deep learning for a time-varying VAR model with extension to integrated VAR International Journal of Forecasting (IF 7.022) Pub Date : 2023-10-30 Xixi Li, Jingsong Yuan
This paper proposes a new approach called DeepTVAR that employs a deep learning methodology for vector autoregressive (VAR) modeling and prediction with time-varying parameters. By optimizing the VAR parameters with a long short-term memory (LSTM) network, we retain the Markovian dependence for prediction purposes and make full use of the recurrent structure and powerful learning ability of the LSTM
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Out-of-sample predictability in predictive regressions with many predictor candidates International Journal of Forecasting (IF 7.022) Pub Date : 2023-10-28 Jesús Gonzalo, Jean-Yves Pitarakis
This paper is concerned with detecting the presence of out-of-sample predictability in linear predictive regressions with a potentially large set of candidate predictors. We propose a procedure based on out-of-sample MSE comparisons that is implemented in a pairwise manner using one predictor at a time. This results in an aggregate test statistic that is standard normally distributed under the global
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Obituary: Everette S Gardner Jr International Journal of Forecasting (IF 7.022) Pub Date : 2023-10-28 Robert Fildes, Rob J Hyndman
Abstract not available
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Improving models and forecasts after equilibrium-mean shifts International Journal of Forecasting (IF 7.022) Pub Date : 2023-10-19 Jennifer L. Castle, Jurgen A. Doornik, David F. Hendry
Equilibrium-mean shifts can result from changes in intercepts with constant dynamics, or be induced by shifts in dynamics with non-zero data means, or both. Induced shifts distort parameter estimates and create a discrepancy between the forecast origin and the equilibrium mean, leading to forecast failure and requiring modifications to previous forecast-error taxonomies. Step-indicator saturation can
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A theory-based method to evaluate the impact of central bank inflation forecasts on private inflation expectations International Journal of Forecasting (IF 7.022) Pub Date : 2023-10-12 Luciano Vereda, João Savignon, Tarciso Gouveia da Silva
We propose a theory-based method to assess the impact of central banks’ inflation forecasts on private inflation expectations. We use regressions derived from a leader-follower model with noisy information and public signals. The leader is the Central Bank (CB), which solves a signal extraction problem to estimate the rational expectation of inflation. Private agents then act by solving an analogous
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Forecasting euro area inflation using a huge panel of survey expectations International Journal of Forecasting (IF 7.022) Pub Date : 2023-10-09 Florian Huber, Luca Onorante, Michael Pfarrhofer
In this paper, we forecast euro area inflation and its main components using a massive number of time series on survey expectations obtained from the European Commission’s Business and Consumer Survey. To make the estimation of such a huge model tractable, we use recent advances in computational statistics to carry out posterior simulation and inference. Our findings suggest that including a wide range
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Demand forecasting under lost sales stock policies International Journal of Forecasting (IF 7.022) Pub Date : 2023-10-08 Juan R. Trapero, Enrique Holgado de Frutos, Diego J. Pedregal
Demand forecasting is a crucial task within supply chain management. Stock control policies are directly affected by the precision of probabilistic demand forecasts. For instance, safety stocks and reorder points are based on those forecasts. However, forecasting and replenishment policies have typically been studied separately. In this work, we explore the influence of inventory assumptions on the
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The profitability of lead–lag arbitrage at high frequency International Journal of Forecasting (IF 7.022) Pub Date : 2023-09-30 Cédric Poutré, Georges Dionne, Gabriel Yergeau
Any lead–lag effect in an asset pair implies that future returns on the lagging asset have the potential to be predicted from past and present prices of the leader, thus creating statistical arbitrage opportunities. We utilize robust lead–lag indicators to uncover the origin of price discovery, and we propose an econometric model exploiting that effect with level 1 data of limit order books (LOBs)
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Forecasting crude oil market volatility: A comprehensive look at uncertainty variables International Journal of Forecasting (IF 7.022) Pub Date : 2023-09-29 Danyan Wen, Mengxi He, Yudong Wang, Yaojie Zhang
Uncertainty variables involving diverse aspects play leading roles in determining oil price movements. This study aims to improve the aggregate crude oil market volatility prediction based on a large set of uncertainty variables from a comprehensive viewpoint. Specifically, we apply three shrinkage methods, namely, forecast combination, dimension reduction, and variable selection, to extract valuable
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Systemic bias of IMF reserve and debt forecasts for program countries International Journal of Forecasting (IF 7.022) Pub Date : 2023-09-22 Theo S. Eicher, Reina Kawai
Countries experiencing balance of payments (BOP) crises may obtain IMF loans to stabilize external accounts. These loans require IMF programs that outline performance targets to ensure forecasted recovery trajectories. Two key indicators of external account performance are reserves and short-term external debt (“STdebt”). Extensive literature evaluates IMF forecasts, but reserves and STdebt have not
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Comparing forecasting performance with panel data International Journal of Forecasting (IF 7.022) Pub Date : 2023-09-22 Ritong Qu, Allan Timmermann, Yinchu Zhu
We develop new methods for testing equal predictive accuracy for panels of forecasts, exploiting information in both the time-series and cross-sectional dimensions of the data. We examine general tests of equal forecasting performance averaged across all time periods and individual units, along with tests that focus on subsets of time or clusters of units. Properties of our tests are demonstrated through
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Improving geopolitical forecasts with 100 brains and one computer International Journal of Forecasting (IF 7.022) Pub Date : 2023-09-20 Hilla Shinitzky, Yhonatan Shemesh, David Leiser, Michael Gilead
The ability to accurately predict future events is critical in numerous areas of human life. Past research has shown that human reasoning can usefully predict geopolitical outcomes, but such forecasts are still far from perfect. In the current work, we investigate whether machine learning can help predict whether people’s forecasts are likely to be correct. We rely on data from a geopolitical forecasting
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A multi-task encoder-dual-decoder framework for mixed frequency data prediction International Journal of Forecasting (IF 7.022) Pub Date : 2023-09-10 Jiahe Lin, George Michailidis
Mixed-frequency data prediction tasks are pertinent in various application domains, in which one leverages progressively available high-frequency data to forecast/nowcast the low-frequency ones. Existing methods in the literature tailored to such tasks are mostly linear in nature; depending on the specific formulation, they largely rely on the assumption that the (latent) processes that govern the
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Editorial Board International Journal of Forecasting (IF 7.022) Pub Date : 2023-09-08
Abstract not available
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Network time series forecasting using spectral graph wavelet transform International Journal of Forecasting (IF 7.022) Pub Date : 2023-09-09 Kyusoon Kim, Hee-Seok Oh
We propose a novel method for forecasting network time series that occur in graphs or networks. Our approach is based on a spectral graph wavelet transform (SGWT) that provides the localized behavior of graph signals around each node. The proposed method improves forecasting performance over other existing methods. In particular, the advantages of the proposed method stand out when signals observed
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Should I open to forecast? Implications from a multi-country unobserved components model with sparse factor stochastic volatility International Journal of Forecasting (IF 7.022) Pub Date : 2023-09-04 Ping Wu
In this paper, we assess whether and when multi-country studies pay off for forecasting inflation and output growth. Factor stochastic volatility is adopted to allow for cross-country linkages and model economies jointly. We estimate factors and rely on post-processing, rather than expert judgement, to obtain an estimate for the number of factors. This is different from most existing two-step approaches
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Pearl Judea, Causality: Models, Reasoning, and Inference, Second Edition (2009) International Journal of Forecasting (IF 7.022) Pub Date : 2023-09-04 Feng Li
Abstract not available
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Guest editorial: In memory of Professor John Edward Boylan, 1959–2023 International Journal of Forecasting (IF 7.022) Pub Date : 2023-08-28 Aris Syntetos, Robert Fildes, Ivan Svetunkov
Abstract not available
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Harry Markowitz: An appreciation International Journal of Forecasting (IF 7.022) Pub Date : 2023-08-16 John Guerard
Harry Markowitz passed on June 22, 2023; some four years short of reaching 100 years old. Dr. Markowitz was not a traditional economist. That fact was well- established and documented from his thesis defense at the University of Chicago. When Milton Friedman uttered lines to the effect that Harry’s thesis has nothing wrong with it, but is not an economics dissertation, Dr. Friedman applied a very narrow
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A False Discovery Rate approach to optimal volatility forecasting model selection International Journal of Forecasting (IF 7.022) Pub Date : 2023-08-06 Arman Hassanniakalager, Paul L. Baker, Emmanouil Platanakis
Estimating financial market volatility is integral to the study of investment decisions and behaviour. Previous literature has, therefore, attempted to identify an optimal volatility forecasting model. However, optimal volatility forecasting is dynamic. It depends on the asset being studied and financial market conditions. We propose a novel empirical methodology to account for this dynamism. Using
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(Structural) VAR models with ignored changes in mean and volatility International Journal of Forecasting (IF 7.022) Pub Date : 2023-07-21 Matei Demetrescu, Nazarii Salish
The paper discusses how standard forecasting tools in multivariate time series analysis are affected when ignoring possible changes in the mean and the (co)variance. We study the estimation, forecasts, and estimated impulse responses of so-called long vector autoregressions, for which the complexity of the model increases with the sample size. We prove that, in spite of structural change in the data
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Bayesian forecasting in economics and finance: A modern review International Journal of Forecasting (IF 7.022) Pub Date : 2023-07-18 Gael M. Martin, David T. Frazier, Worapree Maneesoonthorn, Rubén Loaiza-Maya, Florian Huber, Gary Koop, John Maheu, Didier Nibbering, Anastasios Panagiotelis
The Bayesian statistical paradigm provides a principled and coherent approach to probabilistic forecasting. Uncertainty about all unknowns that characterize any forecasting problem – model, parameters, latent states – is able to be quantified explicitly and factored into the forecast distribution via the process of integration or averaging. Allied with the elegance of the method, Bayesian forecasting
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Quantifying subjective uncertainty in survey expectations International Journal of Forecasting (IF 7.022) Pub Date : 2023-07-09 Fabian Krüger, Lora Pavlova
An increasing number of household and firm surveys ask for subjective probabilities that the inflation rate falls into various outcome ranges. We provide a new measure of the uncertainty implicit in such probabilities. The measure has several advantages over existing methods: It is robust, trivial to implement, requires no functional form assumptions, and is well-defined for all logically possible