Forecasting With Model Uncertainty: Representations and Risk Reduction

Hirano, Keisuke; Wright, Jonathan H.

doi:10.3982/ecta13372

Cited by 29 publications

(16 citation statements)

References 43 publications

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“…For bagging λ =1, while various Bayesian predictors, including Bayesian model averaging and empirical Bayes can also be formulated in this manner, by setting λ appropriately. Interestingly, Hirano and Wright () show that forecasting models constructed using out‐of‐sample or split sample schemes perform well only when combined with other methods, such as bagging. Broadly speaking, their results offer a glimpse into the benefits of using state‐of‐the‐art (asymptotic) statistical analysis in order to examine new methods that combine conventional out‐of‐sample approaches to model selection and estimation with algorithmic approaches such as bagging.…”

Section: Dimension Reduction and Penalized Regressionmentioning

confidence: 99%

“…A closely related strand considers shrinkage (penalized regression) methods, including the likes of ridge regression, the least absolute shrinkage selection operator (lasso), the elastic net and the non‐negative garrote. These and other shrinkage related methods are discussed in Bai and Ng (, ), Schumacher (), Stock and Watson (), Kim and Swanson (, ) and Hirano and Wright (), for example. Broadly speaking, the number of such methods available to empiricists is now immense.…”

Section: Introductionmentioning

confidence: 99%

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Big data analytics in economics: What have we learned so far, and where should we go from here?

Swanson

Xiong

2018

Canadian J of Economics

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Research into predictive accuracy testing remains at the forefront of the forecasting field. One reason for this is that rankings of predictive accuracy across alternative models, which under misspecification are loss function dependent, are universally utilized to assess the usefulness of econometric models. A second reason, which corresponds to the objective of this paper, is that researchers are currently focusing considerable attention on so-called big data and on new (and old) tools that are available for the analysis of this data. One of the objectives in this field is the assessment of whether big data leads to improvement in forecast accuracy. In this survey paper, we discuss some of the latest (and most interesting) methods currently available for analyzing and utilizing big data when the objective is improved prediction. Our discussion includes a summary of various so-called dimension reduction, shrinkage and machine learning methods as well as a summary of recent tools that are useful for ranking prediction models associated with the implementation of these methods. We also provide a brief empirical illustration of big data in action, in which we show that big data are indeed useful when predicting the term structure of interest rates.Résumé. L'analyse des données massives en économie : ce qu'on a appris jusqu'à maintenant et quelles directions pour la prochaine étape? Les travaux sur les tests de précision des prévisions demeurent au premier plan dans le monde de la prévision. Une première raison est que les ordonnancements de la précision des prévisions des divers modèles (qui selon le degré de mis-spécification dépend de la fonction de perte) sont universellement utilisés pour calibrer l'utilité des modèles économétriques. Une seconde raison, qui correspond à l'objectif de ce mémoire, est que les chercheurs concentrent une portion considérable de leur attention sur ce qu'on appelle les données massives, et les outils (anciens et nouveaux) disponibles pour analyser ce type de données. Un des objectifs dans ce champ d'études est d'établir si les données massives mènent à participants of the 2017 Deutsche Bundesbank Workshop on Forecasting for useful comments and suggestions.l'amélioration dans la précision des prévisions. Dans cette étude-synthèse, les auteurs examinent quelques-unes des méthodes les plus récentes et les plus intéressantes qui sont disponibles pour analyser et utiliser ce genre de données quand l'objectif est d'améliorer les prévisions. La discussion inclut une présentation succincte d'approches en termes de réduction de dimensions, de rétrécissement, et méthodes d'apprentissage machine, ainsi qu'un résumé succinct d'outillages récents qui sont utiles pour ordonnancer les modèles de prévision associés à la mise en application de ces méthodes. On fournit aussi une brève illustration de données massives en action, dans laquelle on montre que l'analyse des données massives est utile pour prévoir la structure par échéance des taux d'intérêt.JEL classification: C12, C22, C534 In their setu...

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Section: Dimension Reduction and Penalized Regressionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Big data analytics in economics: What have we learned so far, and where should we go from here?

Swanson

Xiong

2018

Canadian J of Economics

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“…Source of Model Uncertainty. The uncertainty of the DDM arises from a series of factors, including wrong settings of the model, imperfect input information, and the inherent randomness of events and behaviors [21]. The PM and EPT are combined to establish the DDM, which fully embodies the comprehensive socioeconomic benefits of pipeline projects.…”

Section: Uncertainty Of Dominance Degree Modelmentioning

confidence: 99%

Uncertainty Analysis for Natural Gas Transport Pipeline Network Layout: A New Methodology Based on Monte Carlo Method

Zeng

Sun²,

Zhu

et al. 2018

Journal of Advanced Transportation

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Natural gas plays an increasing important role in the China’s energy revolution. The rapid market development and refined government regulation demand improvements in the natural gas transport pipeline network. Therefore, it is of great theoretical and practical significance to conduct a study regarding the layout of pipeline networks. To reflect the comprehensive benefits of pipeline projects and obtain global optimal solution, this study introduces the dominance degree model (DDM). Aiming at optimizing the layout of natural gas transport pipeline networks, this paper studies the uncertainty of the DDM and the corresponding method for network layout. This study proposes an uncertainty analysis based on the Monte Carlo method to quantify the uncertainty of the DDM and its influential factors. Finally, the methodology is applied to the real case of a natural gas transport pipeline project in Zhejiang Province, China. The calculation results suggest that the methodology appropriately addresses the problem of pipeline network layout for natural gas transport. This has important implications for other potential pipeline networks not only in the Zhejiang Province but also throughout China and beyond.

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“…Second, machine learning methods have been employed to generate new statistical procedures specifically tailored for economic applications in recent work by Belloni, Chen, Chernozhukov, and Hansen (2012), Belloni, Chernozhukov, and Hansen (2014), Chernozhukov, Hansen, and Spindler (2015), Fan, Liao, and Yao (2015), Hirano and Wright (2017) and Caner and Kock (2018), to name a few. Boosting is one of the most successful machine learning methods.…”

Section: Introductionmentioning

confidence: 99%

Boosting the Hodrick-Prescott Filter

Phillips¹,

Shi

2019

SSRN Journal

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The Hodrick-Prescott (HP) filter is one of the most widely used econometric methods in applied macroeconomic research. The technique is nonparametric and seeks to decompose a time series into a trend and a cyclical component unaided by economic theory or prior trend specification. Like all nonparametric methods, the HP filter depends critically on a tuning parameter that controls the degree of smoothing. Yet in contrast to modern nonparametric methods and applied work with these procedures, empirical practice with the HP filter almost universally relies on standard settings for the tuning parameter that have been suggested largely by experimentation with macroeconomic data and heuristic reasoning about the form of economic cycles and trends. As recent research has shown, standard settings may not be adequate in removing trends, particularly stochastic trends, in economic data. This paper proposes an easyto-implement practical procedure of iterating the HP smoother that is intended to make the filter a smarter smoothing device for trend estimation and trend elimination. We call this iterated HP technique the boosted HP filter in view of its connection to L 2-boosting in machine learning. The paper develops limit theory to show that the boosted HP filter asymptotically recovers trend mechanisms that involve unit root processes, deterministic polynomial drifts, and polynomial drifts with structural breaks-the most common trends that appear in macroeconomic data and current modeling methodology. In doing so, the boosted filter provides a new mechanism for consistently estimating multiple structural breaks. A stopping criterion is used to automate the iterative HP algorithm, making it a data-determined method that is ready for modern data-rich environments in economic research. The methodology is illustrated using three real data examples that highlight the differences between simple HP filtering, the data-determined boosted filter, and an alternative autoregressive approach. These examples show that the boosted HP filter is helpful in analyzing a large collection of heterogeneous macroeconomic time series that manifest various degrees of persistence, trend behavior, and volatility.

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Forecasting With Model Uncertainty: Representations and Risk Reduction

Cited by 29 publications

References 43 publications

Big data analytics in economics: What have we learned so far, and where should we go from here?

Big data analytics in economics: What have we learned so far, and where should we go from here?

Uncertainty Analysis for Natural Gas Transport Pipeline Network Layout: A New Methodology Based on Monte Carlo Method

Boosting the Hodrick-Prescott Filter

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