Mikkel Plagborg-Møller, Princeton University

"Instrumental Variable Identification of Dynamic Variance Decompositions"


Empirical macroeconomists often estimate impulse response functions using external instruments (proxy variables) for the shocks of interest. However, existing methods do not answer the key question of how important the shocks are in driving macro aggregates, unless the researcher is willing to assume either a Structural Vector Autoregressive representation or that the shocks are directly observed. We provide tools for doing inference on forecast variance decompositions in a general semiparametric moving average model, disciplined only through the availability of valid external instruments. We show that the share of the forecast variance that can be attributed to a shock is partially identified, albeit with informative bounds. Point identification of most parameters of interest, including historical decompositions, can be achieved under weaker assumptions than invertibility (i.e., the SVAR model assumption). The degree of invertibility is also set-identified; hence, invertibility is testable. To perform inference, we construct easily computable, partial identification robust confidence intervals. We illustrate our methods using (i) a structural macro model and (ii) an empirical study of the importance of monetary policy shocks in U.S. data.

Contact person: Rasmus Søndergaard Pedersen