The US economy is on track to expand by 2.1% in this year’s first quarter, based on The Capital Spectator’s new median point forecast for several econometric estimates (real seasonally adjusted annual rate). The projection is incrementally below GDP’s 2.2% rise during 2014’s fourth-quarter.

Today’s revised 2.1% estimate for Q1, however, is well below last month’s 2.8% forecast. Recent predictions from other sources also reflect downgraded Q1 GDP data. For instance, The Wall Street Journal’s March survey-based estimate for Q1 GDP sees a 2.3% advance for this year’s first three months — down from the expected 2.7% rise in last month’s update.

The government’s initial Q1 GDP report is scheduled for release on April 29, which leaves a bit more than a month for new data and revised projections for growth.

Meantime, 2014’s Q4 GDP is in focus this week: the government’s third estimate for growth in last year’s final quarter is due on Friday (Mar. 27). Econoday.com’s consensus forecast anticipates that 2014:Q4 GDP growth will be revised up slightly to 2.4% vs. the 2.2% rise in last month’s release from the Bureau of Economic Analysis.

Here’s a graphical summary of how The Capital Spectator’s Q1:2015 estimate compares with recent history and forecasts from other sources:

Here are the various forecasts that are used to calculate CapitalSpectator.com’s median estimate:

As updated estimates are published, based on incoming economic data, the chart below tracks the changes in the evolution of the projections.

Finally, here’s a brief profile for each of The Capital Spectator’s GDP forecast 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 forecast GDP. In addition to the data quartet in the 4-factor model, the 10-factor forecast also incorporates the following six series: ISM Manufacturing PMI Composite Index, housing starts, initial jobless claims, the stock market (Wilshire 5000), 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 forecast 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 forecast, 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 forecast 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 forecast. 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.