Working Paper: NBER ID: w11064
Authors: Martin D. D. Evans
Abstract: This paper describes a method for calculating daily real-time estimates of the current state of the U.S. economy. The estimates are computed from data on scheduled U.S. macroeconomic announcements using an econometric model that allows for variable reporting lags, temporal aggregation, and other complications in the data. The model can be applied to find real-time estimates of GDP, inflation, unemployment or any other macroeconomic variable of interest. In this paper I focus on the problem of estimating the current level of and growth rate in GDP. I construct daily real-time estimates of GDP that incorporate public information known on the day in question. The real-time estimates produced by the model are uniquely-suited to studying how perceived developments the macro economy are linked to asset prices over a wide range of frequencies. The estimates also provide, for the first time, daily time series that can be used in practical policy decisions.
Keywords: Real-time estimates; GDP; Macroeconomic announcements; Kalman filter
JEL Codes: E3; C3
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| macroeconomic data releases (E01) | real-time GDP estimates (E01) |
| timing and nature of macroeconomic announcements (E60) | real-time GDP growth estimates (E20) |
| real-time GDP growth estimates (E20) | asset price movements (G19) |