Simulation, Estimation and Selection of Mixed Causal-Noncausal Autoregressive Models: The MARX Package
18 Pages Posted: 10 Aug 2017
Date Written: August 9, 2017
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.
Keywords: MARX, mixed causal-noncausal autoregressive process, t-MLE, estimation, model selection, simulation, R
JEL Classification: C22, E31, E37
Suggested Citation: Suggested Citation