The US economy is expected to post a substantial rebound in the government’s initial estimate of second-quarter GDP that’s scheduled for release on Wednesday, according to the Capital Spectator’s median econometric nowcast. Following the surprisingly sharp latest prediction, based on the average forecast of 48 economists.
Here’s a graphical review of how The Capital Spectator’s updated Q2 nowcast compares with recent history and forecasts from other sources:
Next, let’s review the individual nowcasts that are used to calculate the median estimate:
As updated nowcasts are published, the chart below tracks the changes for context in assessing how the projections are evolving.
Finally, here’s a brief profile for each of The Capital Spectator’s GDP nowcast methodologies:
R-4: This estimate is based on a 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 (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).
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ARIMA GDP: The econometric engine for this nowcast is known as an “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 R to crunch the numbers.
ARIMA R-NIPA: The model uses an “forecast” package, which optimizes the parameters based on the data set’s historical record.