First-quarter US GDP is expected to increase 3.2%, according to the latest update of The Capital Spectator’s average econometric nowcast. That’s virtually unchanged from the previous 3.1% nowcast, published on March 19. (GDP percentage changes are quoted as real seasonally adjusted annual rates.)
Today’s average Q1 nowcast represents a sharp increase from GDP’s 0.4% rise in last year’s Q4, according to the final estimate reported by the Bureau of Economic Analysis. The government’s first estimate of this year’s Q1 GDP is scheduled for release on April 26.
The faster pace of growth in GDP reflected in the current Q1 nowcast reflects upbeat numbers in several indicators reported so far this year through February. Personal income and spending in February, for instance, posted decent gains, as did housing starts and industrial production. Private-sector payrolls also advanced at a moderately faster rate in January and February relative to the trend in 2012. But the incoming data for March has been quite a bit softer, including Friday’s sluggish jobs report for last month, a mixed report on the ISM Manufacturing Index for March, and an upturn in jobless claims in recent weeks.
Will the remaining economic updates for March lower the next Q1 GDP nowcast, which will be released just ahead of the BEA’s initial estimate on April 26? If the actual numbers available for March so far are a guide, that’s a distinct possibility. Accordingly, the next round of economic releases deserve close attention, including Thursday’s weekly update on jobless claims and Friday’s report on retail sales for March.
Meanwhile, here’s how The Capital Spectator’s average Q1:2013 nowcast compares with other estimates and actual data in recent history:
Next, here’s a look at The Capital Spectator’s individual nowcasts that are used to compute the average estimate:
Here’s a recap of how the Q1 nowcasts have evolved so far:
Finally, here’s 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.