The post-Hurricane Sandy economic updates have taken a slight toll on the GDP outlook for the fourth quarter. Since our previous Q4:2012 nowcast on November 5, the average estimate for real GDP growth has slipped to 1.2% from 1.7% previously, based on five econometric methodologies (see list below). That compares with the actual 2.0% increase for Q3, according to the government’s announcement last month. (All percentage changes cited based on quarter-over-quarter data in annualized terms). Keep in mind that there’s still a long way to go until the release of the initial estimate of Q4 GDP on January 30, 2013 from the Bureau of Economic Analysis. Meantime, if the data favors us with a degree of post-hurricane bounce back, the nowcasts will rise in the weeks ahead. Turning back to the present, let's take a closer look at how The Capital Spectator’s current nowcasts stack up.
Today's estimates of Q4 GDP growth rates are below those in recent survey forecasts from The Wall Street Journal (WSJ) and the Philadelphia Fed’s Survey of Professional Forecasters (SPF). Our average 1.2% nowcast is well under the 1.8% predictions published by WSJ and SPF earlier this month.
The decline in today’s nowcasts vs. the November 5 estimates reflects the weak incoming data for October, namely: industrial production and retail sales, which is used as a proxy for the consumer sector until the personal consumption expenditures report for October is published next week.
For now, the latest numbers suggest that we should moderate our expectations for Q4 GDP vs. the previous quarter. There’s a reasonable argument that the updates in the weeks ahead will improve, at least for those of us who think that the hurricane distorted the numbers. Even if that assumption proves overly optimistic, our average 1.2% nowcast implies that slow growth is still the path of least resistance--assuming that the fiscal cliff threat is defused. The mildly optimistic average GDP nowcast also draws support from the latest update for the The Capital Spectator Economic Trend Index, which estimates that recession risk remains low for the moment. All of this is based on reported numbers and using the data to project the near-term outlook. Political factors arising from self-inflicted macro pain via our representatives in Washington, in other words, isn’t part of the statistical analysis. Some hazards for the business cycle can’t be quantified, even if they’re staring us in the face.
As for the numbers in hand, here’s a brief review of how they’re sliced and diced to generate The Capital Spectator’s GDP nowcasts:
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 based on historical data from the early 1970s onward and updates the estimates as new numbers arrive. The methodology here is identical to the 4-factor model except that it uses additional factors—10 in all. In addition to the data quartet 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)
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.
ARIMA 4 Nowcast. This model is similar to the ARIMA technique above in terms of the econometric technique, but with a key difference. Instead of using GDP's historical 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.
The vector autoregression model looks to several data series in search of interdependent relationships for estimating GDP. The historical data sets in the 4-factor and ARIMA 4 models above are also used 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.