How do central banks use IRFs of DSGE models and VARs

sorry for posting a question not related to dynare :). But I hope to find some answers.

My intuition is that…

  1. IRFs of DSGE models are optimal responses of macroeconomic variables to shocks, given that DSGEs are equilibrium models. So it guides policy makers in responding to shocks in an optimal way.

  2. IRFs of VARs are suboptimal responses of macroeconomic variables to shocks because it uses only actual data which contains past suboptimal decisions of economic agents (households, government, central bank, etc). So it guides policy makers in knowing how macroeconomic variables responds to shocks in the data, though these are not necessarily the best responses.

As far I as know, IRFs of DSGEs and VARs differ, sometimes considerably. DSGE-VAR models kinda bridge the two, but my question is about DSGEs and VARs and how central banks uses IRFs of these two models. I haven’t worked in a central bank before :slight_smile:

  1. You description above is wrong. It’s not about optimality. If you estimate a DSGE model on actual data, you typically still assume that the agents in it are behaving optimal.
  2. The econometric difference between a VAR and a DSGE model is mostly the presence of cross-equation restriction. See e.g.
  1. From a central bank’s perspective, the biggest difference between the two is the fact that DSGE models are robust to the Lucas critique. That means you can do (policy) counterfactuals in them. That is not valid with VARS as they are not structural models.
  2. A second advantage of DSGE models for central banks is that they allow you to tell narratives about how the world works in a consistent way. That eases communication to policy-makers and the public. With a VAR it’s mostly about which curve goes up or down, while the DSGE model tells you about the underlying economic decisions.
  3. That of course comes with a downside as the structure of the model implies restriction on the data that may not be satisfied in practice (due to model misspecification). In that case, DSGE-VARs allow relaxing the rigid cross-equation restrictions of DSGE-models while still using informative priors in a VAR. But they are mostly useful for forecasting.

Hi Prof. Pfeifer, many thanks for the explanations.

It is great if VAR IRFs are consistent with the story told by the DSGE model. If it is inconsistent, then what to do? Is it now dependent on the researcher and what he wants to do?

For example, a positive weather shock in a DSGE model will increase output for an agricultural economy, theoretically (thus, a theoretical story). In a VAR model, it may go up or down or not respond at all. If it goes down, then what to do?

  1. Change the story in the DSGE model? Thus if you believe the VAR model is correct.
  2. Or change the VAR model? Thus if you want to keep the theoretically consistent story of the DSGE model…

It very much depends on your prior. Conflicting evidence always is a problem. If the DSGE and the sVAR differ, there can be two reasons:

  1. The sVAR does not correctly identify the shocks/dynamics.
  2. The DSGE model is misspecified
    Depending on whether you think 1 or 2 is more likely you would either continue to work on the model or VAR.
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