Working Paper: CEPR ID: DP10610
Authors: Massimiliano Marcellino; Vasja Sivec
Abstract: Large scale factor models have been often adopted both for forecasting and to identify structural shocks and their transmission mechanism. Mixed frequency factor models have been also used in a reduced form context, but not for structural applications, and in this paper we close this gap. First, we adapt a simple technique developed in a small scale mixed frequency VAR and factor context to the large scale case, and compare the resulting model with existing alternatives. Second, using Monte Carlo experiments, we show that the finite sample properties of the mixed frequency factor model estimation procedure are quite good. Finally, to illustrate the method we present three empirical examples dealing with the effects of, respectively, monetary, oil, and fiscal shocks.
Keywords: estimation; identification; impulse response function; mixed frequency data; structural FAVAR; temporal aggregation
JEL Codes: C32; C43; E32
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Monetary policy shocks (E39) | Quarterly GDP growth (O49) |
| Oil price shocks (Q43) | Quarterly GDP (E20) |
| Government expenditure shocks (H59) | Employment (J68) |
| Government expenditure shocks (H59) | Consumer spending (D12) |