NOTE : AFTER READING THIS, ALSO TAKE A LOOK AT : “The effect of data revisions on the NBER recession model” and “Estimating Recession Probabilities using GDP/GDI”
The National Buro for Economic Research (NBER) are the final arbiters of recession dating in the U.S. They take forever to proclaim specific starts and ends to expansions so all the revisions can “work their way through” and they can be dead accurate. Given these proclamation lags can take up to 12 months, their announcements are good for historical, academic and back-testing use only. Now given that many reputable people are claiming we (1) are already in recession or (2) are about to enter one, let us discard all our fancy models aside and look hard at what the NBER will be looking at.
The NBER does not define a recession in terms of two consecutive quarters of decline in real GDP. Rather, a recession is a significant decline in economic activity spread across the economy, lasting more than a few months, normally visible in real GDP, real income, employment, industrial production, and wholesale-retail sales.
They will be examining 4 monthly co-incident indicators:
- Industrial Production
- Real personal income less transfers deflated by personal consumption expenditure
- Non-farm payrolls
- Real retail sales deflated by consumer price index
Less weight is given by the NBER for items 1 and 4 as they are sectoral measurements for manufacturing and retail as opposed to broad measures of the economy. The NBER also give a lot of attention to the two broadest measures of the economy, Gross Domestic product (GDP) and Gross Domestic Income (GDI) which are however published quarterly. We provide a separate analysis “GDPI Report” that examines these two components in detail as described in our research note “Estimating Recession Probabilities from GDP/GDI.”
The aim of this research note is to apply traditional recession forecasting and probability modelling techniques to the 4 co-incident monthly indicators examined by NBER so that we can “see what the NBER are seeing.” Bear in mind, the 4 components are co-incident and thus the recession model we build is likely to be at least 1-month lagging in its determinations. The aim here is not “real-time forecasting” of recessions (we use the Recession Forecasting Ensemble for this) but to obtain “confirmation of last resort” that we are indeed in recession.
The data obtained for the 4 monthly co-incident NBER indicators are taken from these monthly updated charts at the Federal Reserve Bank of St Louis which gives “some idea” about if we are in recession or not but is a bit difficult to determine how far from recession we are. The 2nd determination is a bit more important to the stock market operator or fund manager than the first.
The model we will built and demonstrate here “Monthly NBER Model” is combined with our quarterly (but with monthly revisions) “GDPI Model” to give subscribers a comprehensive view on ALL the indicators the NBER will be examining in their business cycle determinations. In most cases the models will provide recession signals up to 8 months before the NBER themselves proclaim official dates.
2. Real Personal Income
In this instance we have found the 3-month smoothed growth rate to work the best for signalling recession. This is short enough to be sensitive to rapid changes in personal incomes without whipsawing you will false positives, as shown below:
3. Non-farm payroll employment
In this instance we have found the 3-month smoothed growth rate less 0.415 to work the best for signalling recession. This is short enough to be sensitive to rapid changes in employment without whipsawing you will false positives, as shown in the chart below:
4. Real Retail Sales
In this instance we have found the 12-month % change (not smoothed) growth rate to work the best for signalling recession. A shorter period results in too many false positives. The growth is shown below:
5. Syndrome Diffusion Index
We have also found 4 thresholds below which each co-incident indicators’ growth shown above must respectively fall to contribute to a recession “syndrome.” They are as follows:
- Industrial Production -1.77%
- Personal Income -0.25%
- Payroll Employment +0.89%
- Retail Sales +0.44%
When each indicator falls below its syndrome trigger, it means nothing on its own and merely counts a vote toward the Syndrome Index. We then take 2 and subtract the number of votes to get a Recession Syndrome Diffusion Index recession dating model. When the index falls below zero (more than 2 systems are below their syndrome triggers) we call recession. The syndrome triggers were obtained from an optimization program to find the values that maximize the Area Under Curve (AUC or ROC) of the resulting recession dating model. These are the values for which the model provides the least amount of false positives, the least amount of false negatives and the highest recession/expansion sorting score. The use of this index considerably enhances the accuracy of the NBER recession dating model we are leading up to:
6.Building the weighted Composite NBER Model
We now have 5 components with which to build a multi-factor composite co-incident index:
- Industrial Production growth (17% weighting)
- Real personal income growth (31% weighting)
- Non-farm payrolls growth (30% weighting)
- Real retail sales growth (12% weighting)
- Syndrome Diffusion Index (10% weighting)
We can now take all 6 time series, namely Industrial production, Retail Sales, Employment, Personal Incomes, Syndrome Diffusion Index and the weighted composite and put them through a Probit statistical process to develop a 6-factor recession probability model as shown below:
The recession model weighted composite we built in section-6 was based on an optimization running through the entire data set, namely 1959 through to July 2012. As part of normal rigorous statistical testing we need to ensure that this optimization was not a lucky random configuration and that the 4 co-incident indicators in an optimized weighted average model do indeed consistently reflect behavior that accurately identifies recessions.
Economic time series used in measuring business cycles and forecasting recessions are subject to revisions and re-benchmarking. Over time, more up-to-date and accurate data become available and time series are revised to reflect the updates. Some economic time series are subject to more drastic revisions than others. For example, the unemployment rate is subject to far smaller revisions than a broader time series such as GDP which is known to encounter very large revisions. Each instance of an update or revision to a time series is referred to as a “vintage”. Generally every time-series spawns a vintage on a weekly, monthly or quarterly basis depending on how often it is published.