Working Paper: CEPR ID: DP5178
Authors: Domenico Giannone; Lucrezia Reichlin; David Small
Abstract: This paper formalizes the process of updating the nowcast and forecast on output and inflation as new releases of data become available. The marginal contribution of a particular release for the value of the signal and its precision is evaluated by computing 'news' on the basis of an evolving conditioning information set. The marginal contribution is then split into what is due to timeliness of information and what is due to economic content. We find that the Federal Reserve Bank of Philadelphia surveys have a large marginal impact on the nowcast of both inflation variables and real variables and this effect is larger than that of the Employment Report. When we control for timeliness of the releases, the effect of hard data becomes sizeable. Prices and quantities affect the precision of the estimates of GDP while inflation is only affected by nominal variables and asset prices.
Keywords: factor model; forecasting; large datasets; monetary policy; news; real time data
JEL Codes: C33; C53; E52
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| increased data availability (C82) | more accurate nowcasts (C53) |
| Federal Reserve Bank of Philadelphia surveys (E39) | nowcast of inflation (E31) |
| Federal Reserve Bank of Philadelphia surveys (E39) | nowcast of real variables (C29) |
| timeliness of data releases (C82) | precision of the nowcast (C53) |
| quality of data (C80) | nowcast accuracy (C53) |
| interest rates (E43) | precision of GDP estimates (E20) |
| interest rates (E43) | precision of inflation estimates (E31) |