US GDP is expected to rise 2.0% in this year’s third quarter (real seasonally adjusted annual rate), according to The Capital Spectator’s updated average econometric nowcast. Today’s revision, which is based on the latest economic data, is moderately above the previous 1.7% nowcast average for Q3, which was published on August 26. As additional economic indicators are updated and revised, the nowcast will continue to evolve ahead of the government’s initial estimate of Q3 GDP, which is scheduled for release on October 30.

Several Q3 GDP forecasts from other sources are also predicting a rise of roughly 2.0%. For example, The Wall Street Journal’s latest consensus estimate, based on a survey of economists in early September, projects a 2.1% increase for Q3.
One thing that most forecasts share in common at the moment is the expectation that GDP growth in the third quarter will decelerate from Q2’s 2.5% pace, as reported in the August 29 estimate from the Bureau of Economic Analysis (BEA). Speaking of Q2, tomorrow the government releases its third estimate of GDP for the May-through-June 2013 period—economists expect that BEA will report a slightly faster growth rate: 2.6%, according to Briefing.com’s consensus forecast.
As for looking ahead, here’s how The Capital Spectator’s new Q3 nowcast compares with actual data and several recent forecasts from other sources:

Here’s a look at the individual nowcasts:

Here’s the update history of The Capital Spectator’s Q3 nowcasts to date:

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.