Robust Estimation for ARMA models

By Nora Muler, D. Pena, Victor J. Yohai. 2009

92 citations recorded by the source corpus.

This paper introduces a new class of robust estimates for ARMA mod- els. They are M-estimates, but the residuals are computed so that the eect of one outlier is is limited to the period where it occurs. These estimates are closely related to those based on a robust …lter, but they have two important advantages: they are consistent and the asymptotic theory is tractable. We perform a Monte Carlo where we show that these estimates compare favorable with respect to standard M-estimates and to estimates based on a diagnostic procedure.

Subfields: Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/e27b0dd8f9f8d3ae0387b1c051d62ec18a82c72a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Finance

1. Amélie Charles
2. Olivier Darné
3. Jae Hyun Kim
4. K. Lim
5. Andrew Urquhart
6. Robert Hudson
7. Ayoub Ammy-Driss
8. Matthieu Garcin

In Statistics and Probability

1. Visa Koivunen
2. Yacine Chakhchoukh
3. Abdelhak M. Zoubir
4. Olivier Darné
5. Michael Muma
6. Amélie Charles

Citations in the corpus

• Volatility estimation for Bitcoin: Replication and robustness (2019, methodology): “The bounded innovation propagation (BIP)-AR(1) proposed by Muler, Peña and Yohai (2009) and the BIP-GARCH(1,1) of Muler and Yohai (2008) is used to obtain robust estimates of µ t and σ 2 t respectively in equations (1) and (3).”
• Robust Estimation in Signal Processing: A Tutorial-Style Treatment of Fundamental Concepts (2012, methodology): “In [66], it is shown that this model is equivalent to using a robust filter cleaner.”