Machine Learning Time Series Regressions with an Application to Nowcasting

Babii, Andrii; Ghysels, Éric; Striaukas, Jonas

doi:10.48550/arxiv.2005.14057

Cited by 3 publications

(4 citation statements)

References 31 publications

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“…Interestingly, our study complement theirs in terms of documenting that the (news-based) RF method is better than both the LASSO and the PCA across nearly all outcome variables and forecasting horizons. In contrast, Babii et al (2020) propose a new sparse-group LASSO estimator, and show that it performs favorably compared to other alternatives, especially when combined with using text as data, when nowcasting US GDP growth.…”

Section: Introductionmentioning

confidence: 99%

“…By using ML techniques to form predictions our analysis also relates to recent research by Medeiros et al (2019) and Babii et al (2020). Whereas Medeiros et al (2019) use the FRED-MD dataset to compare ML models for inflation forecasting, we focus on the (textual) news versus hard economic data dimension when forecasting National Account Statistics.…”

Section: Introductionmentioning

confidence: 99%

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News media versus FRED‐MD for macroeconomic forecasting

Ellingsen

Larsen

Thorsrud

2021

J of Applied Econometrics

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Using a unique dataset of 22.5 million news articles from the Dow Jones Newswires Archive, we perform an in depth real-time out-of-sample forecasting comparison study with one of the most widely used data sets in the newer forecasting literature, namely the FRED-MD dataset. Focusing on U.S. GDP, consumption and investment growth, our results suggest that the news data contains information not captured by the hard economic indicators, and that the news-based data are particularly informative for forecasting consumption developments.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

News media versus FRED‐MD for macroeconomic forecasting

Ellingsen

Larsen

Thorsrud

2021

J of Applied Econometrics

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“…Related Literature. The use of ridge regression is in fact common in the forecasting literature: Inoue and Kilian (2008) use ridge regularization for forecasting U.S. consumer price inflation and argue that it compares favorably with bagging techniques; De Mol et al (2008) obtain the ridge estimators in the Bayesian context for the purposes of forecasting; Ghosh et al (2019) again study the Bayesian ridge, this time however in the high-dimensional context; Coulombe (2020), Babii et al (2020) and Medeiros et al (2021) compare LASSO, ridge and other machine learning techniques for forecasting with large economic datasets. The ridge penalty is considered within a more general mixed 1 -2 penalization setting in Smeekes and Wijler (2018), who discuss the performance and robustness of penalized estimates for forecasting purposes.…”

Section: Introductionmentioning

confidence: 99%

Ridge Regularized Estimation of VAR Models for Inference

Ballarin¹

2021

Preprint

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Developments in statistical learning have fueled the analysis of high-dimensional time series. However, even in low-dimensional contexts the issues arising from ill-conditioned regression problems are well-known. Because linear time series modeling is naturally prone to such issues, I propose to apply ridge regression to the estimation of dense VAR models.Theoretical non-asymptotic results concerning the addition of a ridge-type penalty to the least squares estimator are discussed, while standard asymptotic and inference techniques are proven to be valid under mild conditions on the regularizer. The proposed estimator is then applied to the problem of sieve approximation of VAR(∞) processes under moderately harsh sample sizes. Simulation evidence is used to discuss the small sample properties of the ridge estimator (RLS) when compared to least squares and local projection approaches: I use a Monte Carlo exercise to argue that RLS with a lag-adapted cross-validated regularizer achieve meaningfully better performance in recovering impulse response functions and asymptotic confidence intervals than other common approaches.

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“…An exception is Babii et al (2020) who recently used the sparse-group lasso to accommodate the dynamic nature of high-dimensional, mixed-frequency data. Nonetheless, they address univariate MIDAS regressions, leaving convex regularization of MF-VARs unexplored.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Regularizers for Mixed-Frequency Vector Autoregressions

Hecq¹,

Marie²,

Wilms³

2021

Preprint

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Mixed-frequency Vector AutoRegressions (MF-VAR) model the dynamics between variables recorded at different frequencies. However, as the number of series and high-frequency observations per lowfrequency period grow, MF-VARs suffer from the "curse of dimensionality". We curb this curse through a regularizer that permits various hierarchical sparsity patterns by prioritizing the inclusion of coefficients according to the recency of the information they contain. Additionally, we investigate the presence of nowcasting relations by sparsely estimating the MF-VAR error covariance matrix. We study predictive Granger causality relations in a MF-VAR for the U.S. economy and construct a coincident indicator of GDP growth.

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Machine Learning Time Series Regressions with an Application to Nowcasting

Cited by 3 publications

References 31 publications

News media versus FRED‐MD for macroeconomic forecasting

News media versus FRED‐MD for macroeconomic forecasting

Ridge Regularized Estimation of VAR Models for Inference

Hierarchical Regularizers for Mixed-Frequency Vector Autoregressions

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