We take a systematic approach to descriptive macroeconomic analysis. There are many sets of information that have value, especially when compared to lagging, noisy, and heavily revised data, like GDP and nonfarm payrolls. Our activity indices do the work of tracking where the US economy stands in real-time, at a daily frequency, updating the index precisely on the date when new data comes out. We use our activity indices to systematically monitor:
The scale of economic growth, acceleration, and inflection (the "rate of change in acceleration"). Each of these is critical for describing the economic activity patterns with the appropriate scale of nuance. Right now our indices reveal that US economic growth is (1) still meaningfully positive, (2) slowing down, but also (3) stabilizing around a still-positive growth rate.
Different data types, including inflation-adjusted "real" economic data, current dollar "nominal" economic data, and "soft" survey data. We also split out our indices across these data types to give a clean understanding of how much "soft" data diverges from "hard" data and how much inflation might be causing divergence between nominal and inflation-adjusted measures of economic growth.
Different sectors of critical cyclical relevance, including manufacturing, housing, services, consumer-facing, and the labor market. We systematically account for sectoral relevance, and not just in terms of alleged share of GDP. We pay keen attention to sectoral contributions to output and employment volatility, which often can be understated by looking solely at GDP shares.
Different degrees of data noise, with some indicators requiring longer (weighted) moving averages and growth rates relative to others.
Different degrees of data timeliness depending on (1) when the data is released relative to its observation period, (2) the degree of filtering needed to smooth out data noise, and (3) the degree to which data can be retrospectively revised.
