Structural VAR models are powerful tools in macroeconomic time series modeling. However, given their vast applications, it is important that they are properly implemented to address the characteristics of their underlying data. In today’s blog, we build on our previous discussions of SVAR models to examine the use of SVAR in the special case of conditional heteroscedasticity. We will look more closely at:
- Conditional heteroscedasticity.
- The impacts of conditional heteroscedasticity on SVAR models.
- Estimating structural impulse response functions (SIRF) in the presence of conditional heteroscedasticity.
- An application to the global oil market.