Simulation, Estimation and Selection of Mixed Causal-Noncausal Autoregressive Models: The MARX Package

Alain Hecq, Lenard Lieb, Sean Telg

Research output: Contribution to JournalArticleAcademic

Abstract

This paper presents the MARX package for the analysis of mixed causal-noncausal autoregressive processes with possibly exogenous regressors. The distinctive feature of MARX models is that they abandon the Gaussianity assumption on the error term.

This deviation from the Box-Jenkins approach allows researchers to distinguish backward (causal) and forward-looking (noncausal) stationary behavior in time series (see e.g. Hecq et al., 2016, for an overview). The MARX package offers functions to simulate, estimate and select mixed causal-noncausal autoregressive models, possibly including exogenous regressors. The procedures for this are discussed in Hecq et al. (2016) for the MAR, and Hecq et al. (2017) for the MARX respectively.
Original languageEnglish
JournalSSRN Electronic Journal
DOIs
Publication statusPublished - Aug 2017

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simulation
time series
analysis

Keywords

  • MARX
  • mixed causal-noncausal autoregressive process
  • t-MLE
  • estimation
  • model selection
  • Simulation
  • R

Cite this

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title = "Simulation, Estimation and Selection of Mixed Causal-Noncausal Autoregressive Models: The MARX Package",
abstract = "This paper presents the MARX package for the analysis of mixed causal-noncausal autoregressive processes with possibly exogenous regressors. The distinctive feature of MARX models is that they abandon the Gaussianity assumption on the error term.This deviation from the Box-Jenkins approach allows researchers to distinguish backward (causal) and forward-looking (noncausal) stationary behavior in time series (see e.g. Hecq et al., 2016, for an overview). The MARX package offers functions to simulate, estimate and select mixed causal-noncausal autoregressive models, possibly including exogenous regressors. The procedures for this are discussed in Hecq et al. (2016) for the MAR, and Hecq et al. (2017) for the MARX respectively.",
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author = "Alain Hecq and Lenard Lieb and Sean Telg",
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Simulation, Estimation and Selection of Mixed Causal-Noncausal Autoregressive Models: The MARX Package. / Hecq, Alain; Lieb, Lenard; Telg, Sean.

In: SSRN Electronic Journal, 08.2017.

Research output: Contribution to JournalArticleAcademic

TY - JOUR

T1 - Simulation, Estimation and Selection of Mixed Causal-Noncausal Autoregressive Models: The MARX Package

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AB - This paper presents the MARX package for the analysis of mixed causal-noncausal autoregressive processes with possibly exogenous regressors. The distinctive feature of MARX models is that they abandon the Gaussianity assumption on the error term.This deviation from the Box-Jenkins approach allows researchers to distinguish backward (causal) and forward-looking (noncausal) stationary behavior in time series (see e.g. Hecq et al., 2016, for an overview). The MARX package offers functions to simulate, estimate and select mixed causal-noncausal autoregressive models, possibly including exogenous regressors. The procedures for this are discussed in Hecq et al. (2016) for the MAR, and Hecq et al. (2017) for the MARX respectively.

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