| 1. |
- Athanasopoulos, George, et al.
(författare)
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Editorial : Innovations in hierarchical forecasting
- 2024
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Ingår i: International Journal of Forecasting. - : Elsevier. - 0169-2070 .- 1872-8200. ; 40:2, s. 427-429
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
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| 2. |
- Athanasopoulos, George, et al.
(författare)
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Forecast reconciliation : A review
- 2024
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Ingår i: International Journal of Forecasting. - : Elsevier. - 0169-2070 .- 1872-8200. ; 40:2, s. 430-456
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Forskningsöversikt (refereegranskat)abstract
- 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 condition is for forecasts to be coherent: to respect the constraints. The past decades have seen substantial growth in this field with the development of reconciliation methods that ensure coherent forecasts and improve forecast accuracy. This paper serves as a comprehensive review of forecast reconciliation and an entry point for researchers and practitioners dealing with hierarchical time series. The scope of the article includes perspectives on forecast reconciliation from machine learning, Bayesian statistics and probabilistic forecasting, as well as applications in economics, energy, tourism, retail demand and demography.
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| 3. |
- Athanasopoulos, George, et al.
(författare)
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On the evaluation of hierarchical forecasts
- 2023
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Ingår i: International Journal of Forecasting. - : Elsevier. - 0169-2070 .- 1872-8200. ; 39:4, s. 1502-1511
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Tidskriftsartikel (refereegranskat)abstract
- The aim of this paper is to provide a thinking road-map and a practical guide to researchers and practitioners working on hierarchical forecasting problems. Evaluating the performance of hierarchical forecasts comes with new challenges stemming from both the structure of the hierarchy and the application context. We discuss several relevant dimensions for researchers and analysts: the scale and units of the time series, the issue of intermittency, the forecast horizon, the importance of multiple evaluation windows and the multiple objective decision context. We conclude with a series of practical recommendations.
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| 4. |
- Cross, Jamie L., et al.
(författare)
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Macroeconomic forecasting with large Bayesian VARs : Global-local priors and the illusion of sparsity
- 2020
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Ingår i: International Journal of Forecasting. - : Elsevier. - 0169-2070 .- 1872-8200. ; 36:3, s. 899-915
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Tidskriftsartikel (refereegranskat)abstract
- A class of global-local hierarchical shrinkage priors for estimating large Bayesian vector autoregressions (BVARs) has recently been proposed. We question whether three such priors: Dirichlet-Laplace, Horseshoe, and Normal-Gamma, can systematically improve the forecast accuracy of two commonly used benchmarks (the hierarchical Minnesota prior and the stochastic search variable selection (SSVS) prior), when predicting key macroeconomic variables. Using small and large data sets, both point and density forecasts suggest that the answer is no. Instead, our results indicate that a hierarchical Minnesota prior remains a solid practical choice when forecasting macroeconomic variables. In light of existing optimality results, a possible explanation for our finding is that macroeconomic data is not sparse, but instead dense.
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| 5. |
- Geelmuyden Rød, Espen, et al.
(författare)
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A review and comparison of conflict early warning systems
- 2024
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Ingår i: International Journal of Forecasting. - : Elsevier. - 0169-2070 .- 1872-8200. ; 40:1, s. 96-112
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Tidskriftsartikel (refereegranskat)abstract
- We review and compare conflict early warning systems on three dimensions: transparency and accessibility, key parameters, and forecasts. The review reveals a need for improved transparency and accessibility of data and code, considerable variation in key parameters across systems, and significant overlaps in countries with the highest risk. We propose that developing standards and platforms that promote transparency, accessibility, and inter-system cooperation can improve knowledge proliferation and system development to mitigate and prevent political violence.
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| 6. |
- Gefang, Deborah, et al.
(författare)
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Forecasting using variational Bayesian inference in large vector autoregressions with hierarchical shrinkage
- 2023
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Ingår i: International Journal of Forecasting. - : Elsevier. - 0169-2070 .- 1872-8200. ; 39:1, s. 346-363
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Tidskriftsartikel (refereegranskat)abstract
- Many recent papers in macroeconomics have used large vector autoregressions (VARs) involving 100 or more dependent variables. With so many parameters to estimate, Bayesian prior shrinkage is vital to achieve reasonable results. Computational concerns currently limit the range of priors used and render difficult the addition of empirically important features such as stochastic volatility to the large VAR. In this paper, we develop variational Bayesian methods for large VARs that overcome the computational hurdle and allow for Bayesian inference in large VARs with a range of hierarchical shrinkage priors and with time-varying volatilities. We demonstrate the computational feasibility and good forecast performance of our methods in an empirical application involving a large quarterly US macroeconomic data set.
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| 7. |
- Hasselgren, Anton, et al.
(författare)
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Do oil price forecast disagreement of survey of professional forecasters predict crude oil return volatility?
- 2024
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Ingår i: International Journal of Forecasting. - 0169-2070 .- 1872-8200.
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Tidskriftsartikel (refereegranskat)abstract
- This paper explores whether the dispersion in forecasted crude oil prices from the European Central Bank Survey of Professional Forecasters can provide insights for predicting crude oil return volatility. It is well-documented that higher disagreement among forecasters of asset price implies greater uncertainty and higher return volatility. Using several Generalized Autoregressive Conditional Heteroskedasticity with Mixed Data Sampling (GARCH-MIDAS) models, we find, based on the in-sample estimation results, the oil market experiences greater volatility when the forecasters’ disagreements increase. The model that integrates both historical realized variance and forward-looking forecaster disagreement into the conditional variance, along with the model focusing solely on pure forward-looking forecaster disagreement, exhibits a much superior fit to the data compared to the model relying solely on realized variance and the models considering forward-looking forecasted mean return. The out-of-sample forecasting results unequivocally illustrate that incorporating forecaster disagreement offers valuable insights, markedly enhancing the predictive accuracy of crude oil return volatility within the GARCH-MIDAS model. Moreover, we illustrate the economic benefit of considering forecasters’ disagreement when forecasting volatility, demonstrating its significance for VaR risk management.
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| 8. |
- Koop, Gary, et al.
(författare)
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Using stochastic hierarchical aggregation constraints to nowcast regional economic aggregates
- 2024
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Ingår i: International Journal of Forecasting. - : Elsevier. - 0169-2070 .- 1872-8200. ; 40:2, s. 626-640
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Tidskriftsartikel (refereegranskat)abstract
- Recent decades have seen advances in using econometric methods to produce more timely and higher frequency estimates of economic activity at the national level, enabling better tracking of the economy in real-time. These advances have not generally been replicated at the sub-national level, likely because of the empirical challenges that nowcasting at a regional level presents, notably, the short time series of available data, changes in data frequency over time, and the hierarchical structure of the data. This paper develops a mixed-frequency Bayesian VAR model to address common features of the regional nowcasting context, using an application to regional productivity in the UK. We evaluate the contribution that different features of our model provide to the accuracy of point and density nowcasts, in particular, the role of hierarchical aggregation constraints. We show that these aggregation constraints, imposed in stochastic form, play a crucial role in delivering improved regional nowcasts; they prove more important than adding region-specific predictors when the equivalent national data are known, but not when this aggregate is unknown.
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| 9. |
- Petropoulos, Fotios, et al.
(författare)
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Forecasting : theory and practice
- 2022
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Ingår i: International Journal of Forecasting. - : Elsevier. - 0169-2070 .- 1872-8200. ; 38:3, s. 705-871
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Forskningsöversikt (refereegranskat)abstract
- Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.
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| 10. |
- Pritularga, Kandrika F., et al.
(författare)
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Shrinkage estimator for exponential smoothing models
- 2023
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Ingår i: International Journal of Forecasting. - : Elsevier. - 0169-2070 .- 1872-8200. ; 39:3, s. 1351-1365
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Tidskriftsartikel (refereegranskat)abstract
- Exponential smoothing is widely used in practice and has shown its efficacy and reliability in many business applications. Yet there are cases, for example when the estimation sample is limited, where the estimated smoothing parameters can be erroneous, often unnecessarily large. This can lead to over-reactive forecasts and high forecast errors. Motivated by these challenges, we investigate the use of shrinkage estimators for exponential smoothing. This can help with parameter estimation and mitigating parameter uncertainty. Building on the shrinkage literature, we explore ℓ1 and ℓ2 shrinkage for different time series and exponential smoothing model specifications. From a simulation and an empirical study, we find that using shrinkage in exponential smoothing results in forecast accuracy improvements and better prediction intervals. In addition, using bias–variance decomposition, we show the interdependence between smoothing parameters and initial values, and the importance of the initial value estimation on point forecasts and prediction intervals.
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