U.S. economic growth for this year's second quarter is widely expected to rebound sharply after a 1.0% decline in Q1. The upbeat estimates for this quarter include the Capital Spectator's median econometric nowcast, which anticipates that GDP will increase 3.3% during the April-through-June period (real seasonally adjusted annual rate). That's up from 2.8% in the previous Q2 nowcast. The Capital Spectator's final estimate for this quarter will be published shortly ahead of the government's initial Q2 GDP report, which will be published on July 30 by the U.S. Bureau of Economic Analysis (BEA).

The expected rebound for Q2 is on track to look even more impressive, based on the lesser estimate that's predicted for this week's revised Q1 data that's scheduled for release on Wednesday, June 25. The consensus forecast via Briefing.com sees Q1 GDP falling at a faster rate in BEA's third revision: a 1.8% slide vs. the previously reported 1.0% decline.

Meantime, here's how The Capital Spectator's updated Q2 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:

As updated nowcasts are published, the chart below will track the changes for context with assessing how the business cycle is evolving in the current quarter.

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 (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 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.