US GDP is expected to increase 3.2%, according to The Capital Spectator’s average econometric nowcast. That’s unchanged from the previous nowcast, published on April 8. (GDP percentage changes are quoted as real seasonally adjusted annual rates.)

Today’s average Q1 nowcast is a sharp increase from the meager 0.4% rise in last year’s fourth quarter, as reported by the Bureau of Economic Analysis. The government’s initial estimate of this year’s Q1 GDP is scheduled for release on Friday, April 26.
The faster pace of growth in today’s GDP nowcast reflects stronger data in several indicators reported so far this year through March. Although some of the data for last month showed signs of weakness—payrolls and the ISM Manufacturing Index, for example—the broad profile still looks encouraging for expecting a substantial improvement in Q1 vs. 2012’s Q4. In fact, several estimates of Q1 GDP from other sources have been raised since The Capital Spectator’s previous nowcast was published. The current Wall Street Journal consensus forecast from economists (based on surveys during April 5-9), for instance, anticipates 3.1% growth for Q1, up from 2.2% in WSJ’s March survey. And the Conference Board estimates Q1 GDP growth at 3.5% in its April 10 estimate, up sharply from 1.6% in its March 13 forecast. In short, a number of forecasters have raised their estimates and moved closer to ~3% level that has been published by The Capital Spectator for several months.
Here’s a graphical look at how our average Q1:2013 nowcast compares with other estimates and actual data in recent history:

Next, here’s a recap of The Capital Spectator’s individual nowcasts that are used to compute the average estimate:

Here’s a recap of how The Capital Spectator’s Q1 nowcasts have evolved to date:

Finally, a brief profile of the methodologies for 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 (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 prediction model based on the data set’s historical record.
ARIMA 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.