Forecasters are in near-universal agreement that the government will report a slowdown in US economic growth in the “advance” GDP report for the first quarter that’s scheduled for release on Wednesday (Apr. 29). The only mystery is the degree of deceleration.
Optimism at the moment is defined by The Capital Spectator’s median estimate: a 1.8% increase for Q1 GDP (real seasonally adjusted annual rate), which represents a decline from the modest 2.2% gain in 2014’s Q4, according to the Bureau of Economic Analysis (BEA). Meantime, today’s revised estimate is moderately below last month’s 2.1% outlook for Q1. Otherwise, it’s all downhill from here when we turn to other sources.
Most economists are expecting a lesser gain for Wednesday’s report. The Wall Street Journal’s latest survey anticipates a tepid 1.4% rise for Q1. Econoday.com’s consensus forecast trims the expected gain to 1.0%. The Atlanta Fed’s GDPNow data pares the outlook to an incremental increase of just 0.1%. There’s even a touch of red ink for one of The Capital Spectator’s estimates that’s used to calculate the median forecast (see table below). The bottom line: if Wednesday’s report reveals that Q2’s growth rate held steady or accelerated, the news will come as surprise to almost everyone.
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 Capital Spectator’s 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.