For another perspective, consider how ETI may evolve as new data is published. One way to project future values for this index is with an econometric technique known as an autoregressive integrated moving average (ARIMA) model, based on calculations via the “forecast” package for R, a statistical software environment.The ARIMA model calculates the missing data points for each indicator, for each month–in this case through Dec. 2015. (Note that Aug. 2015 is currently the latest month with a complete set of published data.) Based on today’s projections, ETI is expected to remain well above its danger zone for the near term. (Keep in mind that frequent business cycle updates are available throughout each month withThe US Business Cycle Risk Report.)
Forecasts are always suspect, of course, but recent projections of ETI for the near-term future have proven to be relatively reliable guesstimates vs. the full set of published numbers that followed. That’s not surprising, given the broadly diversified nature of ETI. Predicting individual components, by contrast, is prone to far more uncertainty in the short run. The current projections (the four black dots on the right in the chart above) suggest that the economy will continue to expand. The chart above also includes the range of vintage ETI projections published on these pages in previous months (blue bars), which you can compare with the actual data that followed, based on current numbers (red dots). The assumption here is that while any one forecast for a given indicator will likely miss the mark, the errors may cancel out to some degree by aggregating a broad set of predictions. That’s a reasonable assumption via the historical record for the ETI forecasts.
For additional perspective on judging the track record of the forecasts, here are the previous updates for the last three months:
Note: ETI is a diffusion index (i.e., an index that tracks the proportion of components with positive values) for the 14 leading/coincident indicators listed in the table above. ETI values reflect the 3-month average of the transformation rules defined in the table. EMI measures the same set of indicators/transformation rules based on the 3-month average of the median monthly percentage change for the 14 indicators. For purposes of filling in the missing data points in recent history and projecting ETI and EMI values, the missing data points are estimated with an ARIMA model.