This paper proposes a new procedure for shock identification of macroeconomic forecasts based on factor analysis. Our identification scheme for information shocks relies on data reduction techniques for daily panels and the recognition that macroeconomic releases exhibit a high level of clustering. A large number of data releases on a single day is of considerable practical interest not only for the estimation but also for the identification of the factor model. The clustering of cross-sectional information facilitates the interpretation of the forecast innovations as real or as nominal information shocks. An empirical application is provided for Swiss inflation. We show that (i) the monetary policy shocks generate an asymmetric response to inflation, (ii) the pass-through for consumer price index inflation is weak, and (iii) the information shocks to inflation are not synchronized.
common factors, daily panels, inflation forecasting