Our current Q4 nowcast is based on the latest economic indicators
available through January 4, which reflects an incomplete profile in
terms of the December numbers. As the remaining updates for last month
roll in, there's a chance that the Q4 nowcast will rise. Several key
reports for December are due next week, including retail sales,
industrial production, and housing starts. But with most of Q4's numbers
already published, the odds are dwindling for a substantial upside
revision for our nowcast. Keep in mind too that if numbers yet to come
are considerably weaker than expected, the Q4 nowcast may fall.
Meantime, here's a look at the individual nowcasts:
Next, here's a recap of how our nowcasts for Q4:2012 GDP have evolved in real time over the last two months:
Finally, here's a brief profile for each of The Capital Spectator's nowcasts:
R-4: This estimate is based on a multiple regression in R
of historical GDP data vs. quarterly changes for four key economic
indicators: real personal consumption expenditures, real personal income
less government transfers, industrial production, and private non-farm
payrolls. The model estimates the statistical relationships from the
early 1970s to the present. The estimates are revised as new data is
published.
R-10: This model also uses a multiple regression framework based on
numbers dating to the early 1970s and updates the estimates as new data
arrives. The methodology is identical to the 4-factor model above,
except that R-10 uses additional factors—10 in all—to nowcast GDP. In
addition to the data quartet in the 4-factor model, the 10-factor
nowcast also incorporates the following six 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)
ARIMA-GDP: The econometric engine for this nowcast is known as an autoregressive integrated moving average.
This ARIMA model uses GDP's history, dating from the early 1970s to the
present, for anticipating the target quarter's change. As the
historical GDP data is revised, so too is the nowcast, which is
calculated in R via the "forecast" package, which optimizes the prediction model based on the data set's historical record.
ARIMA 4: This model is similar to the ARIMA technique above in terms
of the econometric application, but with a key difference. Instead of
using historical GDP data as a lone input, the ARIMA 4 model analyzes
four historical data sets to predict GDP: real personal consumption
expenditures, real personal income less government transfers, industrial
production, and private non-farm payrolls.
VAR-4: This vector autoregression
model uses four data series in search of interdependent relationships
for estimating GDP. The historical data sets in the R-4 and ARIMA 4
models above are also used in VAR-4, albeit with a different econometric
engine. As new data is published, so too is the VAR-4 nowcast. The data
sets range from the early 1970s to the present, using the "vars" package in R to crunch the numbers.