<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si53.gif" display="inline" overflow="scroll"><mml:msub><mml:mrow><mml:mi>ℓ</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:math>-regularization of high-dimensional time-series models with non-Gaussian and heteroskedastic errors

Medeiros, Marcelo C.; Mendes, Eduardo

doi:10.1016/j.jeconom.2015.10.011

Cited by 113 publications

(91 citation statements)

References 29 publications

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“…For LASSO those conditions would be similar to those in the work by Medeiros and Mendes (2016), to which we refer for details. When the number of parameters to be estimated is small relative to T, then the least squares estimator of A 0k and the sample covariance estimator of C 0 can be used to build consistent pre-estimators of 0 , and Assumption 2 is satisfied.…”

Section: Proposition 1 (Selection Consistency and Oracle Property) mentioning

confidence: 99%

“…Note that the notion of sparsity used in this work is different from that used in other papers such as Davis, Zang, and Zheng (2016), Kock and Callot (2015), and Medeiros and Mendes (2016), where sparsity assumptions are formulated for the autoregressive matrices only. We model the panel as a vector autoregression (VAR).…”

Section: Introductionmentioning

confidence: 99%

“…First, it is related to the econometric literature on networks, which includes research by Billio et al (2012), Yılmaz (2014, 2015), Hautsch et al (2015), Anufriev and Panchenko (2015), Härdle et al (2016), and Hagströmer and Menkveld (2016). This paper is also related to the literature on the estimation of sparse VARs (see Basu & Michailidis, 2015;Davis et al, 2016;Kock & Callot, 2015;Medeiros & Mendes, 2016;Song & Bickel, 2011). This paper is also related to the literature on the estimation of sparse VARs (see Basu & Michailidis, 2015;Davis et al, 2016;Kock & Callot, 2015;Medeiros & Mendes, 2016;Song & Bickel, 2011).…”

Section: Introductionmentioning

confidence: 99%

“…Early influential work on networks and panels of time series includes Mantegna (1999). This paper is also related to the literature on the estimation of sparse VARs (see Basu & Michailidis, 2015;Davis et al, 2016;Kock & Callot, 2015;Medeiros & Mendes, 2016;Song & Bickel, 2011). Our contribution also relates to the statistical literature on large-dimensional network estimation based on LASSO techniques.…”

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confidence: 96%

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NETS: Network estimation for time series

Barigozzi

Brownlees

2019

J of Applied Econometrics

130

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Summary We model a large panel of time series as a vector autoregression where the autoregressive matrices and the inverse covariance matrix of the system innovations are assumed to be sparse. The system has a network representation in terms of a directed graph representing predictive Granger relations and an undirected graph representing contemporaneous partial correlations. A LASSO algorithm called NETS is introduced to estimate the model. We apply the methodology to analyze a panel of volatility measures of 90 blue chips. The model captures an important fraction of total variability, on top of what is explained by volatility factors, and improves out‐of‐sample forecasting.

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Section: Proposition 1 (Selection Consistency and Oracle Property) mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 96%

See 2 more Smart Citations

NETS: Network estimation for time series

Barigozzi

Brownlees

2019

J of Applied Econometrics

130

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“…The properties of the Lasso have been studied extensively, as for example in Zhao and Yu (2006), Meinshausen and Bühlmann (2006), Bickel et al (2009), Kock and Callot (2015) and Medeiros and Mendes (2015), to mention just a few. It is known that it only selects the correct model asymptotically under rather restrictive conditions on the dependence structure of the covariates.…”

Section: The Lassomentioning

confidence: 99%

Modeling and Forecasting Large Realized Covariance Matrices and Portfolio Choice

Callot

Kock

Medeiros

2016

J of Applied Econometrics

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Summary We consider modeling and forecasting large realized covariance matrices by penalized vector autoregressive models. We consider Lasso‐type estimators to reduce the dimensionality and provide strong theoretical guarantees on the forecast capability of our procedure. We show that we can forecast realized covariance matrices almost as precisely as if we had known the true driving dynamics of these in advance. We next investigate the sources of these driving dynamics as well as the performance of the proposed models for forecasting the realized covariance matrices of the 30 Dow Jones stocks. We find that the dynamics are not stable as the data are aggregated from the daily to lower frequencies. Furthermore, we are able beat our benchmark by a wide margin. Finally, we investigate the economic value of our forecasts in a portfolio selection exercise and find that in certain cases an investor is willing to pay a considerable amount in order get access to our forecasts. Copyright © 2016 John Wiley & Sons, Ltd.

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Forecasting With Big Dependent Data

2021

Statistical Learning for Big Dependent Data

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ℓ1-regularization of high-dimensional time-series models with non-Gaussian and heteroskedastic errors

Cited by 113 publications

References 29 publications

NETS: Network estimation for time series

NETS: Network estimation for time series

Modeling and Forecasting Large Realized Covariance Matrices and Portfolio Choice

Forecasting With Big Dependent Data

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