Working Paper: CEPR ID: DP5270
Authors: Martin D. D. Evans
Abstract: This paper describes a method for calculating daily real-time estimates of the current state of the US economy. The estimates are computed from data on scheduled US 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: forecasting; GDP; Kalman filtering; real-time data
JEL Codes: C32; E37
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| macroeconomic announcements (E60) | real-time estimates of GDP (E01) |
| real-time estimates of GDP (E01) | asset price movements (G19) |
| lack of timely information (D83) | dynamics of exchange and interest rates (F31) |
| timely estimates (C41) | mitigate issue of lack of timely information (D82) |