Penalized Time Series Regression

Publikation: Bidrag til bog/antologi/rapport/proceedingBidrag til bog/antologiForskningpeer review

Standard

Penalized Time Series Regression. / Kock, Anders Bredahl; Medeiros, Marcelo; Vasconcelos, Gabriel.

Macroeconomic Forecasting in the Era of Big Data. red. / P. Fuleky. Cham : Springer, 2020. s. 193-228 (Advanced Studies in Theoretical and Applied Econometrics, Bind 52).

Publikation: Bidrag til bog/antologi/rapport/proceedingBidrag til bog/antologiForskningpeer review

Harvard

Kock, AB, Medeiros, M & Vasconcelos, G 2020, Penalized Time Series Regression. i P Fuleky (red.), Macroeconomic Forecasting in the Era of Big Data. Springer, Cham, Advanced Studies in Theoretical and Applied Econometrics, bind 52, s. 193-228. https://doi.org/10.1007/978-3-030-31150-6_7

APA

Kock, A. B., Medeiros, M., & Vasconcelos, G. (2020). Penalized Time Series Regression. I P. Fuleky (red.), Macroeconomic Forecasting in the Era of Big Data (s. 193-228). Springer. Advanced Studies in Theoretical and Applied Econometrics, Bind. 52 https://doi.org/10.1007/978-3-030-31150-6_7

CBE

Kock AB, Medeiros M, Vasconcelos G. 2020. Penalized Time Series Regression. Fuleky P, red. I Macroeconomic Forecasting in the Era of Big Data. Cham: Springer. s. 193-228. (Advanced Studies in Theoretical and Applied Econometrics, Bind 52). https://doi.org/10.1007/978-3-030-31150-6_7

MLA

Kock, Anders Bredahl, Marcelo Medeiros og Gabriel Vasconcelos "Penalized Time Series Regression". Fuleky, P. (redaktører). Macroeconomic Forecasting in the Era of Big Data. Cham: Springer. (Advanced Studies in Theoretical and Applied Econometrics, Bind 52). 2020, 193-228. https://doi.org/10.1007/978-3-030-31150-6_7

Vancouver

Kock AB, Medeiros M, Vasconcelos G. Penalized Time Series Regression. I Fuleky P, red., Macroeconomic Forecasting in the Era of Big Data. Cham: Springer. 2020. s. 193-228. (Advanced Studies in Theoretical and Applied Econometrics, Bind 52). https://doi.org/10.1007/978-3-030-31150-6_7

Author

Kock, Anders Bredahl ; Medeiros, Marcelo ; Vasconcelos, Gabriel. / Penalized Time Series Regression. Macroeconomic Forecasting in the Era of Big Data. red. / P. Fuleky. Cham : Springer, 2020. s. 193-228 (Advanced Studies in Theoretical and Applied Econometrics, Bind 52).

Bibtex

@inbook{bedf5f36eff54f8c87bf959f89de5a52,
title = "Penalized Time Series Regression",
abstract = "This chapter covers penalized regression in the framework of linear time series models and reviews the most commonly used penalized estimators in applied work, namely Ridge Regression, the Least Absolute Shrinkage and Selection Operator (Lasso), the Elastic Net, the adaptive versions of the Lasso as well as Elastic Net and the group Lasso. Other penalties are briefly presented. We discuss theoretical properties such as consistent variable selection, the oracle property, and oracle inequalities and list time series models in which penalized estimators have been shown to possess these. Potentially problematic aspects of (some of) these properties are also discussed. Practical issues, such as the selection of the penalty parameters and available computer implementations, are also covered. A Monte Carlo simulation is presented in order to compare different penalties in terms of estimation precision, model selection capability, and forecasting performance. Finally, an application to forecasting US monthly inflation is presented.",
author = "Kock, {Anders Bredahl} and Marcelo Medeiros and Gabriel Vasconcelos",
year = "2020",
doi = "10.1007/978-3-030-31150-6_7",
language = "English",
isbn = "978-3-030-31149-0",
series = "Advanced Studies in Theoretical and Applied Econometrics",
publisher = "Springer",
pages = "193--228",
editor = "P. Fuleky",
booktitle = "Macroeconomic Forecasting in the Era of Big Data",

}

RIS

TY - CHAP

T1 - Penalized Time Series Regression

AU - Kock, Anders Bredahl

AU - Medeiros, Marcelo

AU - Vasconcelos, Gabriel

PY - 2020

Y1 - 2020

N2 - This chapter covers penalized regression in the framework of linear time series models and reviews the most commonly used penalized estimators in applied work, namely Ridge Regression, the Least Absolute Shrinkage and Selection Operator (Lasso), the Elastic Net, the adaptive versions of the Lasso as well as Elastic Net and the group Lasso. Other penalties are briefly presented. We discuss theoretical properties such as consistent variable selection, the oracle property, and oracle inequalities and list time series models in which penalized estimators have been shown to possess these. Potentially problematic aspects of (some of) these properties are also discussed. Practical issues, such as the selection of the penalty parameters and available computer implementations, are also covered. A Monte Carlo simulation is presented in order to compare different penalties in terms of estimation precision, model selection capability, and forecasting performance. Finally, an application to forecasting US monthly inflation is presented.

AB - This chapter covers penalized regression in the framework of linear time series models and reviews the most commonly used penalized estimators in applied work, namely Ridge Regression, the Least Absolute Shrinkage and Selection Operator (Lasso), the Elastic Net, the adaptive versions of the Lasso as well as Elastic Net and the group Lasso. Other penalties are briefly presented. We discuss theoretical properties such as consistent variable selection, the oracle property, and oracle inequalities and list time series models in which penalized estimators have been shown to possess these. Potentially problematic aspects of (some of) these properties are also discussed. Practical issues, such as the selection of the penalty parameters and available computer implementations, are also covered. A Monte Carlo simulation is presented in order to compare different penalties in terms of estimation precision, model selection capability, and forecasting performance. Finally, an application to forecasting US monthly inflation is presented.

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U2 - 10.1007/978-3-030-31150-6_7

DO - 10.1007/978-3-030-31150-6_7

M3 - Book chapter

AN - SCOPUS:85076796765

SN - 978-3-030-31149-0

T3 - Advanced Studies in Theoretical and Applied Econometrics

SP - 193

EP - 228

BT - Macroeconomic Forecasting in the Era of Big Data

A2 - Fuleky, P.

PB - Springer

CY - Cham

ER -