Complete subset regressions with large-dimensional sets of predictors

Elliott, Graham; Gargano, Antonio; Timmermann, Allan

doi:10.1016/j.jedc.2015.03.004

Cited by 44 publications

(30 citation statements)

References 21 publications

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“…In empirical work for stock returns (Elliott et al 2013) and inflation rate, GDP growth, and unemployment rate forecasts (Elliott et al 2015), Elliott et al find that the CSR forecasts perform very well compared to univariate ARIMA (autoregressive integrated moving average) or common factor models. Equal-weighted combinations appear to work as well as alternative combination schemes that estimate the weights of the individual forecasting models and do not sample the models at random if the total number of models is too large to allow all possible models with k predictors to be included in the combination.…”

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confidence: 99%

Forecasting in Economics and Finance

Elliott

Timmermann

2016

Annu. Rev. Econ.

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Practices used to address economic forecasting problems have undergone substantial changes over recent years. We review how such changes have influenced the ways in which a range of forecasting questions are being addressed. We also discuss the promises and challenges arising from access to big data. Finally, we review empirical evidence and experience accumulated from the use of forecasting methods to a range of economic and financial variables.

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confidence: 99%

Forecasting in Economics and Finance

Elliott

Timmermann

2016

Annu. Rev. Econ.

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“…This result shows the feasibility of random subset regression in practice. It also provides a theoretical justification of the results obtained in Elliott et al (2013) and Elliott et al (2015), where it was found that little prediction accuracy is lost by using a finite number of random draws of the subsets.…”

Section: Feasibility Of the Msfe Boundsmentioning

confidence: 88%

“…The covariance matrix of the predictors equals Σ X = 1 p P P , where P is a p × p matrix whose elements are independently and randomly drawn from a standard normal distribution. As argued by Elliott et al (2015), this ensures that the eigenvalues of the covariance matrix are reasonably spaced.…”

Section: Monte Carlo Set-upmentioning

confidence: 98%

“…The set-up we employ is similar to the one by Elliott et al (2015). The data generating process takes the form…”

Section: Monte Carlo Set-upmentioning

confidence: 99%

“…However, when the number of predictors increases, estimating all possible subsets rapidly becomes infeasible. As a practical solution, Elliott et al (2013) and Elliott et al (2015) propose to draw subsets at random and average over the obtained forecasts. We show that there are in fact strong theoretical arguments for this approach, and establish tight bounds on the resulting MSFE.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting Using Random Subspace Methods

Boot

Nibbering

2016

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract Random subspace methods are a novel approach to obtain accurate forecasts in high-dimensional regression settings. We provide a theoretical justification of the use of random subspace methods and show their usefulness when forecasting monthly macroeconomic variables. We focus on two approaches. The first is random subset regression, where random subsets of predictors are used to construct a forecast. Second, we discuss random projection regression, where artificial predictors are formed by randomly weighting the original predictors. Using recent results from random matrix theory, we obtain a tight bound on the mean squared forecast error for both randomized methods. We identify settings in which one randomized method results in more precise forecasts than the other and than alternative regularization strategies, such as principal component regression, partial least squares, lasso, and ridge regression. The predictive accuracy on the high-dimensional macroeconomic FRED-MD data set increases substantially when using the randomized methods, with random subset regression outperforming any one of the above mentioned competing methods for at least 66% of the series. Terms of use: Documents in

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Forecasting sovereign risk in the Euro area via machine learning

Belly

Boeckelmann

Graciano

et al. 2023

Journal of Forecasting

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We test the usefulness of machine learning (ML) for the valuation and pricing of sovereign risk in the Euro area along two important dimensions: i) its predictive accuracy compared with traditional econometric methods, and ii) its assessment of the main economic factors underlying market perceptions of sovereign risk.We find that ML techniques can capture the dynamics inherent in the market valuation of country risk far more efficiently than traditional econometric models, both in the cross-section and in the time series. Moreover, we show that public sentiment about financial news, redenomination fears and the degree of hawkishness/dovishness expressed in the ECB president's speeches are major contributors to sovereign bond spreads. We also confirm that macroeconomic and global financial factors affect sovereign risk assessment and the corresponding formation of sovereign spreads.

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Complete subset regressions with large-dimensional sets of predictors

Cited by 44 publications

References 21 publications

Forecasting in Economics and Finance

Forecasting in Economics and Finance

Forecasting Using Random Subspace Methods

Forecasting sovereign risk in the Euro area via machine learning

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