The case for anticipating slow growth in the third quarter rolls on. Today’s update of The Capital Spectator’s suite of nowcasts for third-quarter real GDP remain steady relative to the previous revisions from September 30. The current numbers incorporate last week’s economic updates for several September indicators, which continue to signal slow growth for the economy (see here and here, for example). The models tell us that when the government releases the official GDP report for Q3 on October 26, the odds still look favorable for expecting that the economy’s real (inflation-adjusted), annualized change will be slightly better than the sluggish 1.3% growth reported for Q3. This outlook is supported by the incoming data for September for estimating the broad economic trend (as we discussed earlier today), which suggests that there’s still forward momentum in the economy overall and that recession risk remains low, given the latest numbers available.
Here’s a review of how today’s nowcasts compare with recent history and two widely cited predictions (via The Wall Street Journal’s survey of economists and the Survey of Professional Forecasters):
There are advantages to keeping on eye on multiple estimates, each drawn from different methodologies, and tracking how they change through time. Are the estimates continually rising, falling, or going every which way? The fact that the nowcasts remain relatively steady as new data is published is an encouraging sign, given that the estimates for Q3:2012 GDP are somewhat higher vs. the reported number for Q2. With that in mind, here’s how our four in-house nowcasts compare in recent weeks:
Here’s a brief profile of how each of The Capital Spectator’s nowcasts are calculated:
4-Factor Nowcast. This estimate is based on a multiple regression of quarterly GDP in history relative to quarterly changes for four key economic indicators: real personal consumption expenditures, real personal income less government transfers, industrial production, and private non-farm payrolls. This model compares the data on a quarterly basis, looking for relationships with GDP within each quarter from the early 1970s to the present. The four independent variables are updated monthly and so the nowcast is revised as new data is published. In effect, this model is telling us what the data trends in the current quarter imply for the quarter’s GDP growth.
10-Factor Nowcast. This model also uses a multiple regression framework for historical data from the early 1970s and updates the estimates as new numbers arrive, but with two key differences relative to the 4-factor model above. First, this model uses more factors—10 in all. In addition to the data quartet used in the 4-Factor model, the 10-Factor nowcast also incorporates the following series:
• ISM Manufacturing PMI Composite Index
• housing starts
• initial jobless claims
• the stock market (S&P 500)
• crude oil prices (spot price for West Texas Intermediate)
• the Treasury yield curve spread (10-year Note less 3-month T-bill)
The second difference is that the 10-factor model analyzes relationships across a longer span of time by considering the average of changes across the trailing one-, two-, three-, and four-quarter comparisons. The intuition here is that there may be influences on GDP that predate activity in the current quarter, and that those influences come from a broader set of economic trends. If so, the 10-factor model will do a better job of capturing those signals relative to the 4-factor model.
ARIMA Nowcast. The econometric engine for this nowcast is known as an autoregressive integrated moving average. The technique is using only real GDP’s history, dating from the early 1970s onward, for anticipating the current quarter’s change. As the most recent quarterly GDP number is revised, so too is the ARIMA nowcast, which is calculated in R software via Professor Rob Hyndman’s “forecast” package, which optimizes the prediction model based on the data set’s historical record.
VAR Nowcast. The vector autoregression model looks to several data series in search of interdependent relationships for estimating GDP. I use the four variables in the 4-factor model noted above to generate VAR nowcasts of GDP. As new data is published, so too is the VAR nowcast. The basic idea here is to let the data specify the model’s parameters. The data sets are based on historical records from the early 1970s, using the “vars” package for R to crunch the numbers.