U.S. Economic Trend Update | 11.19.12

Will the fiscal cliff drag us over the edge? Is the festering crisis in Europe going to explode with severe repercussions for the U.S. economy? Do the wars in the Middle East pose a threat too? All of these risk factors are at the top of the list of things to worry about. The good news is that the economy has, or had, a tailwind, based on the current lineup of economic and financial indicators. That’s no guarantee that economic growth will survive in an increasingly risky world. But hasty decisions about the economy can take a heavy toll as well.

It’s certainly not getting any easier to evaluate the state of the business cycle as 2012 winds down. But if there’s any chance of accurately deciding what’s likely to come next, it’s essential to start the analysis with a broad review of the data. Was the economy deteriorating in recent months? Or was it holding up fairly well? The numbers suggest the latter, as shown in today’s update of the indicators in The Capital Spectator Economic Trend Index (CS-ETI):
Plugging the numbers into a diffusion index tells us that recession risk is still minimal, according to the latest data, as the next chart below shows. In other words, the majority of indicators are still trending positive through October, albeit based on 11 of the 14 indicators in CS-ETI for last month. Yes, the incoming numbers may change the profile from light to dark fairly quickly, although October’s final profile is all but assured to remain relatively encouraging. November and beyond are where the primary mystery begins. If the tide turns for the worse, CS-ETI will lose altitude, and perhaps quickly. But based on what we know at the moment, the bias toward growth is, or at least was, substantial.

The quality and depth of the growth is another matter—a topic that CS-ETI doesn’t address. Rather, the focus here is on quantifying the trend via a wide spectrum of economic and financial indicators in search of one of the more elusive reads in macro: the business cycle, the mother of all latent variables in economics.
Translating CS-ETI’s time series into probabilities across time via a probit model also suggests that recession risk is negligible as of October. But the various risks noted above aren’t necessarily reflected in the current probability estimate. Given the precarious state of affairs at the moment, it’s not clear how much persistence to assume for the trend in the months to come.

Looking ahead is always a murky affair and perhaps more so these days. But with eyes wide open, let’s consider what the data’s implying for the near-term future via a sophisticated econometric technique that’s applied in a relatively straightforward way. In particular, I’ve generated forecasts for each of CS-ETI’s indicators, independently of one another, using an ARIMA model. I then aggregate the results to estimate CS-ETI for the next several months.1 The process starts by filling in the handful of missing numbers for October and then estimating each of the data sets for November and December. It’s safe to assume a fair amount of error for any one of these forecasts, although aggregating the individual estimates can minimize the risk a bit if some of the errors cancel each other out.

As usual, the further out we look, the higher the potential for error generally. That said, the basic message is that CS-ETI isn’t set to tumble, or so the estimated data for the next few months suggest. A similar set of forecasts for CS-ETI using a vector autoregression technique dispenses a similar set of estimates.
We can’t take these forecasts as gospel, of course, but neither we should we dismiss them since the ARIMA estimates for CS-ETI have been encouraging recently. For instance, the chart above shows that the ARIMA estimates of September 28 turned out to be fairly accurate for anticipating August and September’s profile—at a time when there was still a fair amount of uncertainty about how those month profiles would fare, particularly for September.
So, what’s the risk with all of this? The big one is that all the trouble swirling about isn’t reflected in the current data and the potential for a negative shock is growing. For example, the news that investment spending in corporate America continues to fall is a dark sign.
The upbeat estimates for CS-ETI through the end of the year, in other words, may be victimized by new events that aren’t yet reflected in the latest economic reports. That’s always a risk factor, of course, although the potential for negative surprises is probably higher than average at the moment.
Even so, it’s premature to assume that the business cycle for the U.S. is doomed. Yes, the numbers above look counterintuitive compared with how we may “feel” about the economy at the moment, or how the outlook appears by focusing on a relative handful of reports. If we are, in fact, at a turning point that unleashes a new recession there’ll be clear signs of the change via a broad set of the numbers, and soon. Yes, you could assume the worst now and start making decisions accordingly. But that’s a risk factor too, and a rather large and costly one when considered across time.
It’s inevitable that calling major turning points in the business is destined for inaccuracy in real time. There are too many factors working against us to expect a high degree of accuracy at any one moment. The question is how we’d prefer to be wrong as a general proposition? Is it preferable to make premature recession calls? Or are we better served in trying to recognize those times when cycle has turned down after the fact—as early as possible?
My research tells me that the latter is the way to go. Why? Many reasons, including the compelling statistical and econometric evidence that the odds of success are considerably higher for accurately calling the start of new downturns shortly after they’ve begun vs. trying to anticipate these events before they’ve started and/or based on minimal evidence for making such assumptions. That doesn’t mean we can be nonchalant about rising risks that could push us over the edge. But history teaches that the vast majority of error in business cycle analysis is bound up trying to assume too much about what may happen in the months ahead. That’s certainly been a problem during the last several years, which is overflowing with examples of premature warnings of a new recession that, so far, never arrived.


1. The ARIMA forecasts are calculated in R software, using Professor Rob Hyndman’s “forecast” package, which optimizes the model’s parameters based on each data set’s historical record. ^

2 Responses to U.S. Economic Trend Update | 11.19.12

  1. Thank you for sharing your research on recession forecasting and your data
    sources. They gave me a significant head start on my own recession modeling
    efforts. My recession research has raised a few interesting questions and I
    would be interested in your thoughts.
    First, have you considered using a leading indicator variable in your
    diffusion index – either the Government series or ECRI’s WLI series?
    Ironically, ECRI’s 2011 recession call appears to be incorrect or at least
    very premature, but their WLI series does have some explanatory power
    Second, have you ever considered using your diffusion index to estimate a
    probit or logit model to forecast the peak and trough associated with NBER
    recessionary periods? The equity indices tend to reach their peaks before
    recessions begin and bottom out before recessions end. As a result, it is
    more difficult to construct a peak-trough model, but the peak-trough
    forecasts would arguably be more valuable for trading purposes.
    Finally, I found that adding a second independent variable based on the
    recent change in the diffusion index improved the performance of the probit
    model, especially when attempting to estimate the peak-trough model. The
    change variable helps differentiate between entering and exiting the
    recession, both of which could have the same diffusion index value. Have
    you ever considered adding the change in the diffusion index as a second
    independent variable in your probit model?
    Thanks again for your help.
    Brian Johnson

  2. JP says:

    Great questions. My responses:
    1. I don’t use leading indicators in CS-ETI because the design focuses on the source indicators. Using a leading indicator built on several other indexes would be somewhat redundant and therefore risky because CS-ETI’s signals may be distorted. That said, several leading indicators sources are already incorporated in CS-ETI, such as the stock market’s data. I do look at various leading indicator benchmarks and other aggregated business cycle metrics, but I analyze them elsewhere. In the interest of generating clean signals for CS-ETI, I stick with the source data. On that note, I’ve designed the components of CS-ETI to reflect different aspects of economic activity with minimal, if any, redundancy. In the few cases where redundant signals are used–the labor market and consumer spending–I aggregate the source data for a cleaner analysis in computing the diffusion index that is CS-ETI.
    2 & 3. I don’t formally search for peaks and valleys in business cycles, at least not with CS-ETI. This is a worthy pursuit and I do focus on this question elsewhere in my research. But CS-ETI is designed as a simple, transparent, and relatively robust process to highlight recession risk and when that risk is high or low. Modeling the peaks and troughs requires a different approach, and one that CS-ETI isn’t designed to provide.

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