This paper aims to provide a non-technical introduction into the SVAR methodology. Particular emphasize is put on the approach to identification in SVAR models, which is compared to identification in simultaneous equation models. It is shown that SVAR models are useful tools to analyze the dynamics of a model by subjecting it to an unexpected shock, whereas simultaneous equation models are bett...

Conditional heteroskedasticity can be exploited to identify the structural vector autoregressions (SVAR) but the implications for inference on structural impulse responses have not been investigated in detail yet. We consider the conditionally heteroskedastic SVAR-GARCH model and propose a bootstrap-based inference procedure on structural impulse responses. We compare the finite-sample properti...

The purpose of this paper is to provide an introduction to the methodology known as Structural Vector Autoregression (SVAR) analysis and to examine its applicability in the context of Irish macroeconomics. The SVAR approach has been developed over the last decade to interpret business cycle fluctuations and to help identify the effects of different economic policies. It is an extension on the t...

Conventional structural vector autoregressive (SVAR) models with Gaussian errors are not identified, and additional identifying restrictions are typically imposed in applied work. We show that the Gaussian case is an exception in that a SVAR model whose error vector consists of independent non-Gaussian components is, without any additional restrictions, identified and leads to (essentially) uni...

In structural vector autoregressive (SVAR) models identifying restrictions for shocks and impulse responses are usually derived from economic theory or institutional constraints. Sometimes the restrictions are insufficient for identifying all shocks and impulse responses. In this paper it is pointed out that specific distributional assumptions can also help in identifying the structural shocks....

Structural vector autoregressive (SVAR) models have emerged as a dominant research strategy in empirical macroeconomics, but suffer from the large number of parameters employed and the resulting estimation uncertainty associated with their impulse responses. In this paper we propose general-to-specific model selection procedures to overcome these limitations. After showing that single-equation ...

The central finding of the recent structural vector autoregression (SVAR) literature with a differenced specification of hours is that technology shocks lead to a fall in hours. Researchers have used this finding to argue that real business cycle models are unpromising. We subject this SVAR specification to a natural economic test and show that when applied to data from a multiple-shock busines...

The main substantive finding of the recent structural vector autoregression literature with a differenced specification of hours (DSVAR) is that technology shocks lead to a fall in hours. Researchers have used these results to argue that business cycle models in which technology shocks lead to a rise in hours should be discarded. We evaluate the DSVAR approach by asking, is the specification de...

In this paper we present the Large Inverse Cholesky (LIC) method, an efficient method for computing the coefficient matrices of a Structural Vector Autoregressive (SVAR) model.

This paper builds a structural VARMA (SVARMA) model for investigating Canadian monetary policy. Despite the support for a VARMA model for monetary policy analysis, the traditional VAR and SVAR models have predominantly been used in the literature mainly due to difficulties associated with the identification and estimation of such a model. Using the scalar component model (SCM) proposed by Athan...