US GDP is expected to rise 2.0% (real seasonally adjusted annual rate) in this year’s second quarter, according to The Capital Spectator’s average econometric nowcast. Today’s update is a touch lower than the previous 2.1% nowcast average for Q2, published on June 21. The government’s initial estimate of this year’s Q2 GDP is scheduled for release on Wednesday, July 31.
Compared with actual data, the current nowcast is slightly above the first quarter’s previously reported 1.8% increase, based on the June 26 estimate from the US Bureau of Economic Analysis.
As always, point forecasts generally should be viewed skeptically. By contrast, a more valuable piece of information in the nowcast data is the fact that they’ve been sliding. Q2 GDP nowcasts have gradually but consistently decreased since the initial estimate of 2.9%, published on May 6. That’s a sign for managing expectations down generally.
In fact, most analysts are expecting a slowdown in Q2 GDP vs. the previous quarter–in direct contrast with the moderately faster pace implied by The Capital Spectator’s average nowcast. One of the more extreme examples: Morgan Stanley earlier this month cut its Q2 growth estimate for US GDP to a mere 0.3%. The consensus forecast, however, sees somewhat stronger growth of 1.1% for Q2, according to Briefing.com. Nonetheless, analysts generally are expecting a slowdown of consequence in the economy’s expansion in the second quarter.
Keep in mind that Wednesday’s “advance” estimate of Q2 GDP report will also reflect a “comprehensive revision” of the calculation methodology, in part to reflect the rising influence of intellectual capital in economic activity. “We’ve been trying to understand the sources of growth in the GDP,” Steve Landefeld, director of the Bureau of Economic Analysis, tells The New York Times. “One of the longstanding gaps in the numbers has been the contributions of intangibles — creations in the arts and entertainment, research and development, things like that — and what they contribute to GDP.” It’s unclear if the revisions will boost or reduce Wednesday’s estimate relative to what would be reported with the older methodology, although we’ll have a better idea of how it all plays out in a few days.
Meanwhile, here’s a look at how our individual Q2:2013 nowcasts compare with the average estimate:
Next, here’s a graphical review of the current Q2 nowcast in context with actual data and several recent estimates from other sources:
Here’s the update history of The Capital Spectator’s Q2 nowcasts:
Finally, here’s a brief profile for each of The Capital Spectator’s nowcast methodologies:
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 (or real retail sales for the current month until the PCE report is published), 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 parameters based on the data set’s historical record.
ARIMA R-4: This model combines ARIMA estimates with regression anlaysis to project GDP data. The ARIMA 4 model analyzes four historical data sets: real personal consumption expenditures, real personal income less government transfers, industrial production, and private non-farm payrolls. This model uses the historical relationships between those indicators and GDP for projections by filling in the missing data points in the current quarter with ARIMA estimates. As the indicators are updated, actual data replaces the ARIMA estimates and the nowcast is recalculated.
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.
ARIMA R-NIPA: The model uses an autoregressive integrated moving average to estimate future values of GDP based on the datasets of four primary categories of the national income and product accounts (NIPA): personal consumption expenditures, gross private domestic investment, net exports of goods and services, and government consumption expenditures and gross investment. The model uses historical data from the early 1970s to the present for anticipating the target quarter’s change. As the historical numbers are revised, so too is the estimate, which is calculated in R via the “forecast” package, which optimizes the parameters based on the data set’s historical record.