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id.ngml. Non-Gaussian maximum likelihood (NGML) identification of SVAR models. Given an estimated VAR model, this function applies identification by means of a non-Gaussian likelihood for the structural impact matrix B of the corresponding SVAR model.
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The detection of structural shocks in SVARs relies on either economically motivated restrictions or statistical means. We compare alternative identification approaches in a simulation study and in the framework of an empirical analysis of monetary policy in the UK.
An alternative to this approach is to use so-called structural vector autoregressive (SVAR) models, where the relationship between contemporaneous variables is modelled more directly. This post provides an introduction to the concept of SVAR models and how they can be estimated in R.
4 svars: An R Package for Data-Driven Identification in Multivariate Time Series Analysis procedure allows for a smooth transition between the covariance regimes (Lütkepohl and Netsunajev 2017b). The third scheme implements the identification of the structural shocks via conditional heteroskedasticity (Normadin and Phaneuf 2004).
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simple and straightforward ways.2 This paper aims to provide a non-technical introduction into the SVAR analysis. Since many applied macroeconomists are familiar with the use and estimation of traditional structural models like dynamic simultaneous equation models, this paper takes this class of models as a starting point.
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The R package svars, which we describe in this paper, focuses on these statis-tical methods to identify the structural shocks. The R (R Core Team 2021) archive network comprises several widely applied packages for multivariate time series models and, in particular, for analyzing VAR models.
We present the R package svars which implements statistical identification techniques for Structural vector autoregressive (SVAR) models. The package offers both heteroskedasticity based and independence based techniques.
