Semi-Parametric Estimation of Noncausal Vector Autoregression

Christian Gourieroux and Joann Jasiak

No 2015-02, Working Papers from Center for Research in Economics and Statistics

Abstract: This paper introduces consistent semi-parametric estimation methods for mixed causal/noncausal multivariate non-Gaussian processes. We show that in the VAR(1) model, the second-order identification is feasible to some limited extent, contrary to the common belief that non-Gaussian processes are not second-order identifiable. In general, in the mixed VAR (1) it is possible to distinguish the mixed processes with different numbers of causal and noncausal components.For detecting the causal and noncausal components, a semi-parametric exploratory analysis is proposed. It includes a semi-parametric estimation method that does not require any distributional assumptions on the errors. For direct estimation of the matrix of autoregressive coefficients of a VAR (1), we use the generalized covariance estimator. Although this estimator is not fully efficient, it provides the estimates in one single optimization while the MLE requires a number of optimizations, which is equal to the number of all possible causal dimensions. The methods are illustrated by a simulation study.

Keywords: Multivariate Noncausal Process; Identificatio; Semi-Parametric Estimation; Speculative Bubble. (search for similar items in EconPapers)
Pages: 48
Date: 2015-05
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Citations: View citations in EconPapers (5)

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