Robust filtering with quantile regression

João Borges Assunção; Pedro Afonso Fernandes

Robust filtering with quantile regression

João Borges Assunção, Pedro Afonso Fernandes

Resultado de pesquisa

15 Transferências (Pure)

Resumo

This working paper proposes a new, practical method to compute the non-linear Mosheiov-Raveh (MR) filter using least absolute deviations (LAD) instead of the linear programming approach proposed by these two authors. This paper is embodied with an implementation in the R programming language of the proposed method which facilitates the computation of the MR filter in current applications to produce a robust estimate, namely, of the GDP trend growth. This technique may be appropriate to deal with non linear time series or structural changes.

Idioma original	English
Número de páginas	17
Estado da publicação	Publicado - 21 fev. 2022

Acesso ao documento

47725977Final published version, 379 KBLicença: Unspecified

http://hdl.handle.net/10400.14/38332Licença: Unspecified

CUBE: Católica Lisbon Research Unit in Business and Economics
Machado, F. (PI), Aranda, A. M. (Investigador), Costa, A. I. D. A. (Investigador), Martins, A. P. (Investigador), Bernard, A. (Investigador), Fidalgo, A. (Investigador), Reis, C. (Investigador), Abecassis-Moedas, C. (Investigador), Santos, C. (Investigador), Tran, D. (Investigador), Fernandes, D. (Investigador), Bonfim, D. (Investigador), Schliephake, E. (Investigador), Kalogirou, F. (Investigador), Reis, F. (Investigador), Santos, F. (Investigador), Machado, F. (Investigador), Sguera, F. (Investigador), Cerqueiro, G. (Investigador), Reis, H. (Investigador), Barreto, I. (Investigador), Silva, J. (Investigador), Salvado, J. C. (Investigador), Neves, J. C. D. (Investigador), Gijsbrechts, J. (Investigador), Stahl, J. (Investigador), Guedes, J. C. (Investigador), Faias, J. (Investigador), Wagner, L. (Investigador), Correia, I. (Investigador), Saldanha, M. F. (Investigador), Fontes, J. C. (Investigador), Modesto, L. (Investigador), Meira, M. (Investigador), Matos, M. G. D. (Investigador), Gouveia, M. (Investigador), Kozeniauskas, N. (Investigador), Bertani, N. (Investigador), Couzoff, P. (Investigador), Barroso, P. (Investigador), Encarnação, P. (Investigador), Raposo, P. (Investigador), Teles, P. (Investigador), Bastos, W. (Investigador), Bohnsack, R. (Investigador), Vale, R. C. D. (Investigador), Ferreira, R. (Investigador), Lloyd-Braga, T. (Investigador), Ramus, T. (Investigador), Colaço, V. H. (Investigador), Cavicchini, A. (Investigador), Parada, P. (Investigador), Pieters, R. (Investigador), Pinto, F. A. G. D. L. S. (Investigador), Colaço, V. H. (Investigador) & Venter, Z. (Investigador)
1/01/20 → 31/12/24
Projeto

Citação

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title = "Robust filtering with quantile regression",

abstract = "This working paper proposes a new, practical method to compute the non-linear Mosheiov-Raveh (MR) filter using least absolute deviations (LAD) instead of the linear programming approach proposed by these two authors. This paper is embodied with an implementation in the R programming language of the proposed method which facilitates the computation of the MR filter in current applications to produce a robust estimate, namely, of the GDP trend growth. This technique may be appropriate to deal with non linear time series or structural changes.",

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AU - Fernandes, Pedro Afonso

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N2 - This working paper proposes a new, practical method to compute the non-linear Mosheiov-Raveh (MR) filter using least absolute deviations (LAD) instead of the linear programming approach proposed by these two authors. This paper is embodied with an implementation in the R programming language of the proposed method which facilitates the computation of the MR filter in current applications to produce a robust estimate, namely, of the GDP trend growth. This technique may be appropriate to deal with non linear time series or structural changes.

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KW - Non linear time series

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KW - Software

M3 - Working paper

BT - Robust filtering with quantile regression

ER -

Robust filtering with quantile regression

Resumo

Acesso ao documento

Impressão digital

Projetos

CUBE: Católica Lisbon Research Unit in Business and Economics

Citação