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

Research output: Working paperProfessional

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
PublisherSSRN
Number of pages18
Publication statusPublished - 2017

Publication series

SeriesSocial Science Research Network

JEL classifications

  • c22 - "Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models"
  • e31 - "Price Level; Inflation; Deflation"
  • e37 - Prices, Business Fluctuations, and Cycles: Forecasting and Simulation: Models and Applications

Keywords

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

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