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首页> 外文期刊>The Review of Economic Studies >Identification and Estimation in Non-Fundamental S true tor al VARMA Models
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Identification and Estimation in Non-Fundamental S true tor al VARMA Models

机译：非基本S真实or varma模型的识别与估计

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摘要

The basic assumption of a structural vector autoregressive moving average (SVARMA) model is that it is driven by a white noise whose components are uncorrelated or independent and can be interpreted as economic shocks, called "structural" shocks. When the errors are Gaussian, independence is equivalent to non-correlation and these models face two identification issues. The first identification problem is "static" and is due to the fact that there is an infinite number of linear transformations of a given random vector making its components uncorrelated. The second identification problem is "dynamic" and is a consequence of the fact that, even if a SVARMA admits a non-invertibie moving average (MA) matrix polynomial, it may feature the same second-order dynamic properties as a VARMA process in which the MA matrix polynomials are invertible (the fundamental representation). The aim of this article is to explain that these difficulties are mainly due to the Gaussian assumption, and that both identification challenges are solved in a non-Gaussian framework if the structural shocks are assumed to be instantaneously and serially independent. We develop new parametric and semi-parametric estimation methods that accommodate non-fundamentalness in the MA dynamics. The functioning and performances of these methods are illustrated by applications conducted on both simulated and real data.
机译：结构矢量自动增加移动平均线（SVARMA）模型的基本假设是它由诸如众多噪声驱动的，其组件不相关或独立，并且可以被解释为经济冲击，称为“结构”冲击。当误差是高斯时，独立等同于非相关性，并且这些模型面临两个识别问题。第一个识别问题是“静态”，并且是由于存在给定随机向量的无限数量的线性变换，使其组件不相关。第二个识别问题是“动态”，因此，即使SVARMA承认非反转移动平均（MA）矩阵多项式，它也可以具有与Varma过程相同的二阶动态属性MA矩阵多项式是可逆的（基本代表性）。本文的目的是解释这些困难主要是由于高斯假设，并且如果假设结构冲击是即时和连续的结构冲击，则在非高斯框架中解决了识别挑战。我们开发了新的参数和半参数估计方法，以适应MA动态的非基础。这些方法的功能和性能由在模拟和实际数据上进行的应用说明。

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