Debunking Recession Calls
Dwaine van Vuuren | Nov 08, 2012 11:10AM ET
The latest buzz is a 'FRED' chart published by the Federal Reserve of St. Louis depicting Jeremy Pigers’ dynamic factor Markov recession probability index. It recently jumped from less than 1% to 18%. Inferences are being made that recessions have always been underway or occurred very shortly after a reading of 18%. Suddenly the 18% probability went to 100% based on this inference. Why this inference is so ludicrous is aptly described on Jeff Miller's site. Most people publishing these wild 100% recession claims never even bothered to read or understand the academic papers used by the authors to explain the model. Some even went so far as to infer the FED endorses the model and in another case that Piger actually works for the FED. The mind boggles.
Piger and Chauvette are highly respected academics who pioneered the mainstream use of Dynamic Factor Markov models for U.S. recession modelling and co-authored the papers explaining the methodology. By their own admissions, documented in the papers and in separate email communications with me last year, to have a reasonable degree of certainty that you are not witnessing a false positive (wrong signal) you need to make a recession call when at least two or three months have passed with a reading over 80%. I think this is too conservative and would look to two or more months over 50% before I started making inferences.According to Chauvette, “A fast assessment on the state of the economy can be obtained by observing whether the real time probability of recession has crossed the 50% threshold for a month. This rule maximizes the speed at which a turning point might be identified, but increases the chances of declaring a false positive.”
This is because these models can have very volatile data because of revisions of the underlying data and the empirical fact that business cycle phases are persistent, which is explicitly modelled in the Markov-switching framework. What this means is that a bad month of data will be assigned an even higher probability that it is a recession month if the following month is also bad. Similarly, a bad month of data will be assigned an even lower probability of recession if the following months are less bad. So, as more bad or good data is revealed, this increases/decreases the probability that earlier bad months were recessions. So you have this “double re-visioning” effect.
Below is a chart of this effect on Pigers’ model, based on vintage data he sent me when I first suggested to him a while back that it would be nice to start tracking vintages. Clearly, some bad data came amount in the last summer swoon and probabilities climbed accordingly and then subsequent months provided better data and the prior months were revised down accordingly. The revision to the probabilities is big (from 4.8% to 1% for example is a major move)
Chauvette does not publish vintages for her model but I have a data file received from her in late 2011 for some research we were doing that I used to compare to her latest data set (smoothed probabilities)
Here we see that six months of over 25% readings never led to a recession. We also see how dramatic revisions occur from the “empirical persistence” built into these models when August 2011 and Sept 2011 were downgraded from 28% odd to less than 5% This is not a revision, it is a complete “re-think”!
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One final thing you need to consider with both Chauvette and Pigers’ DFMS models is that because of a two-month delay in the availability of the manufacturing and trade sales series, the probabilities of recession are also available only with a two-month delay. So the reading you see now applies to the economy as of August 2012. We’ve had one set of extra data readings since then that have not been incorporated into these models. They are shown below together with a preferred metric we use for sales, namely the REAL RETAIL SALES figure from FRED which has a monthly frequency to give us more timely data. We can see that three of the four were up strongly in the last month.
Both Chauvette and Piger maintain and update these Markov models separately and I have been following them diligently for 18 months. To cater for the volatility of the readings, some smoothing is applied to eliminate false positives. Despite referencing the same academic papers they co-authored, the two models maintained by these academics provide for different readings over time so they do not appear identical. Marcelle Chauvette informs me that the models are the the same but they are using a different series for employment, and a different estimation method.
Apart from the “revision effects” inherent in persistence models, I have discovered using probability indexes over time that small revisions changes in the underlying data can amplify recession probabilities significantly. Because probabilities are so “sensitive” (i.e. close to zero the one minute and then sky-rocketing the next) due to the mathematical methods/formulas used to determine them, you can have significant spikes in recession probabilities only to see them plunging again after a minor data revision. The revision may not look like much when you look at the underlying data but it amplifies the effect on the probabilities. For this reason we prefer to inspect the underlying index data to make inferences and the recession probabilities are more of an “interest factor” or another component into our overall inference.
It is clear that the amplification effect of revisions on recession probabilities coupled with the possible large “re-estimations” made to probabilities from the Markov models’ empirical persistence estimation characteristics, makes it ludicrous to jump to the conclusions many bloggers are currently coming to – namely that a reading of 18% has always only been seen during a recession or just before one and therefore the probability of recession is 100%. If these types of inferences are going to be made, then they should rather be made against the backdrop of the real-time (un-revised) record of DFMS probabilities as depicted below. As you can see, making recession calls based on an observation of 20% will lead to many false calls.
Look, our firm is not a Pollyanna when it comes the the U.S economy and we are not denying there is some informational content in the models’ high readings for August. These academics are well respected and know what they are doing and have been running these models for some time now. The recovery is tepid, but it’s still a recovery by many, many measures. For everyone out there whipping up a negative chart as vindication for the U.S being in recession, you can whip out five more positive charts in defence of expansion. We’ve done just this in Effects of revisions on the NBER recession model ). The latest report that we provide subscribers on this model is attached below for your interest. It confers we have a tepid recovery printing new low-watermarks for expansion and that recession risks have risen to around 6%.
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