Mixed causal–noncausal autoregressions with exogenous regressors

Bibliographic Data

ID21653513
AuthorsAlain Hecq (0000-0002-0548-8215, Department of Quantitative Economics School of Business and Economics, Maastricht University Maastricht The Netherlands), João Victor Issler (0000-0002-1745-1196, Graduate School of Economics—EPGE Getulio Vargas Foundation Rio de Janeiro Brazil), Sean Telg (Department of Econometrics and Operations Research Vrije Universiteit Amsterdam Amsterdam The Netherlands, corresponding author)
Year2020
Volume35
Issue3
Pages328-343
Publication date2020-04-01
Open AccessYes
TypeARTICLE
VenueJournal of Applied Econometrics (JOURNAL)
Journal identifiersISSN: 1099-1255 • E-ISSN: 0883-7252
PublisherWiley (PUBLISHER • GB)
DOI10.1002/jae.2751
OpenAlexW2999080001
LanguageEN
Citations received1
References cited20

Mixed causal–noncausal autoregressive (MAR) models have been proposed to model time series exhibiting nonlinear dynamics. Possible exogenous regressors are typically substituted into the error term to maintain the MAR structure of the dependent variable. We introduce a representation including these covariates called MARX to study their direct impact. The asymptotic distribution of the MARX parameters is derived for a class of non‐Gaussian densities. For a Student likelihood, closed‐form standard errors are provided. By simulations, we evaluate the MARX model selection procedure using information criteria. We examine the influence of the exchange rate and industrial production index on commodity prices

Autoregressive model · Commodity · Covariate · Econometrics · Economics · Gaussian · Lag · Model selection · Nonlinear system · Statistics · Computer Science · Italy: Economic History and Contemporary Issues · Market Dynamics and Volatility · Mathematics · Monetary Policy and Economic Impact

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