Mixed Causal-Noncausal Autoregressions with Strictly Exogenous Regressors

Hecq, Alain and Issler, João Victor and Telg, Sean (2017): Mixed Causal-Noncausal Autoregressions with Strictly Exogenous Regressors.

Abstract

The mixed autoregressive causal-noncausal model (MAR) has been proposed to estimate economic relationships involving explosive roots in their autoregressive part, as they have stationary forward solutions. In previous work, possible exogenous variables in economic relationships are substituted into the error term to ensure the univariate MAR structure of the variable of interest. To allow for the impact of exogenous fundamental variables directly, we instead consider a MARX representation which allows for the inclusion of strictly exogenous regressors. We develop the asymptotic distribution of the MARX parameters. We assume a Student's t-likelihood to derive closed form solutions of the corresponding standard errors. By means of Monte Carlo simulations, we evaluate the accuracy of MARX model selection based on information criteria. We investigate the influence of the U.S. exchange rate and the U.S. industrial production index on several commodity prices.

• Item Type: MPRA Paper
• Language: English
• Keywords: Mixed causal-noncausal process, non-Gaussian errors, identification, rational expectation models, commodity prices
• Subjects: C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes; E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E31 - Price Level ; Inflation ; Deflation; E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E37 - Forecasting and Simulation: Models and Applications

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