Statistical inference for independent component analysis: Application to structural VAR models

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• 作者：Christian Gourié ; roux^a ; ^b ; ^{christian.gourieroux@ensae.fr" class="auth_mail" title="E-mail the corresponding author} ; Alain Monfort^a ; ^{alain.monfort@ensae.fr" class="auth_mail" title="E-mail the corresponding author} ; Jean-Paul Renne^c ; ^{jeanpaul.renne@unil.ch" class="auth_mail" title="E-mail the corresponding author}
• 关键词：C14 ; C32
• 刊名：Journal of Econometrics
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：196
• 期：1
• 页码：111-126
• 全文大小：791 K

文摘

The well-known problem of non-identifiability of structural VAR models disappears if the structural shocks are independent and if at most one of them is Gaussian. In that case, the relevant estimation technique is the Independent Component Analysis (ICA). Since the introduction of ICA by Comon (1994), various semi-parametric estimation methods have been proposed for “orthogonalizing” the error terms. These methods include pseudo maximum likelihood (PML) approaches and recursive PML. The aim of our paper is to derive the asymptotic properties of the PML approaches, in particular to study their consistency. We conduct Monte Carlo studies exploring the relative performances of these methods. Finally, an application based on real data shows that structural VAR models can be estimated without additional identification restrictions in the non-Gaussian case and that the usual restrictions can be tested.