The winter is taking a toll on the outlook for GDP growth in this year’s first quarter. The US economy is expected to expand by 2.4% (real seasonally adjusted annual rate) in the first three months of 2014, according to The Capital Spectator’s new median econometric nowcast. The projected gain is down slightly from the previous 2.6% nowcast for Q1:2014, which was published on February 10.

The lesser pace of growth isn’t surprising. Weaker-than-expected economic numbers have dominated the macro news this month, including yesterday’s report of a sharp slowdown in the growth rate for the services sector, according to Markit’s flash purchasing managers’ index for February. Plugging the new data into our nowcasting models since the previous update has delivered the predictable result. The crucial mystery is whether the downturn in economic activity is a temporary affair that’s related to the weather vs. something more ominous. If a spring revival is in the works, future GDP nowcasts will reflect the cyclical thaw. But if the optimists are wrong, the telltale signs will emerge in the next batch of estimates. Either way, the truth will out as the nowcasts are revised with incoming data and we move closer to the “advance” estimate for Q1 GDP from the US Bureau of Economic Analysis (BEA) that’s scheduled for release on April 30, 2014.

Meantime, here’s how The Capital Spectator’s second Q1 nowcast compares with recent history and several forecasts from other sources:

Next, let’s review the individual nowcasts that are used to calculate the median estimate:

Here’s how the Q1:2014 nowcast updates compare so far:

Finally, here’s a brief profile for each of The Capital Spectator’s GDP 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), and 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 analysis to project GDP data. The ARIMA R-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 R-4 models noted 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.