April | 2024 | RecessionAlert

Archive | April, 2024

What if we adjusted U.S GDP to account for record “war-time” budget deficits?

By RecessionALERT on April 26, 2024 in Reflections

NOTE : this is a very simplistic thought experiment, as determining recessions are usually much more multidimensional , nuanced affairs. Its also debatable whether deficits do indeed artificially prop up the economy or not, or even if they are bad or not. There are also potential multiplier effects with budget deficits that are not taken […]

Read full story · Comments are closed

The SAHM Rule Redux

By RecessionALERT on April 1, 2024 in Research Papers

1.Introduction In macroeconomics, the Sahm rule, or Sahm rule recession indicator, is a heuristic measure by the United States’ Federal Reserve for determining when an economy has entered a recession. It is useful in real-time evaluation of the business cycle and relies on monthly unemployment data from the Bureau of Labor Statistics (BLS). It is […]

Read full story · Comments are closed

IMPORTANT DISCLAIMERS: Although many of our models rely heavily on back-testing, optimisation and probability methods, please note that past performance is NO GUARANTEE for future returns. No system devised by man can predict the future, let alone the future of the markets and economies. What we have are sets of mathematical models that use historical data and varying theories to pinpoint places in time when the STATISTICAL LIKELYHOOD of calling the start and end to recessions is the greatest. In the midst of all the best mathematical models, you can have geo-political events, wars, terrorist attacks, natural disasters and even nuclear accidents that tip everything upside down. This is always a risk factor you need to factor in on any recession call no matter how confident you are in a signal. The SuperIndexes are created through mathematical and statistical optimisation techniques that best fit the historical NBER data. There is no guarantee that out-of-sample performance will match that of prior in-sample performance.