A general drawback of vector autoregressive (VAR) models is that the number of estimated coefficients increases disproportionately with the number of lags. Therefore, fewer information per parameter is available for the estimation as the number of lags increases. In the Bayesian VAR literature one approach to mitigate this so-called curse of dimensionality is stochastic search variable selection (SSVS) as proposed by George et al. (2008). The basic idea of SSVS is to assign commonly used prior variances to parameters, which should be included in a model, and prior variances close to zero to irrelevant parameters. By that, relevant parameters are estimated in the usual way and posterior draws of irrelevant variables are close to zero so that they have no significant effect on forecasts and impulse responses. This is achieved by adding a hierarchial prior to the model, where the relevance of a variable is assessed in each step of the sampling algorithm.1
Work in progress (September 2023). I will try to update this page over the next few months.
This section is intended to provide an overview of the relevant issues in (macro)economic time series analysis. Again the standard disclaimer: This site does not replace a good textbook, but it should help you to get a grasp of the basic concepts more quickly than if you learned it on your own.
The intended structure of this site is: