US economic growth is still expected to decelerate in this year’s fourth quarter relative to Q3, although today’s revised nowcast reflects a stronger outlook for GDP in the final three months of 2014 vs. last month’s projection.  Q4’s advance is now on track for a 3.0% advance (real seasonally adjusted rate), based on The Capital Spectator’s current median point forecast for several econometric estimates. The latest outlook represents a solid improvement over the 2.1% rise in the initial nowcast for Q4.

Today’s update suggests that the outlook for the fourth-quarter is improving, but the current prediction remains well below the previous quarter’s 3.9% gain, according to the Nov. 25th GDP report for Q3 via the Bureau of Economic Analysis (BEA). The gap, however, appears to be closing.

The source for today’s upward revision is a run of better-than-expected economic reports for November. Notably, payrolls, retail sales, and industrial production posted surprisingly strong numbers in the latest round of updates. It’s still debatable if we’re witnessing a genuine acceleration in growth that will be sustained in the new year. One reason for caution: the housing market hasn’t joined the party, at least not yet. Nonetheless, recent numbers have had a bullish influence over The Capital Spectator’s GDP modeling for Q4.

The improvement looks compelling, but no one should mistake the revival as universal among dismal scientists at the moment. Indeed, today’s revised nowcast remains modestly higher vs. several estimates from other sources. The Wall Street Journal’s survey of economists this month, for example, anticipates 2.5% growth for the Q4 GDP report that the BEA will publish next month — an estimate that represents a slight downtick from the Journal’s November survey.

The outlook for the year’s final GDP data remains a work in progress, but some analysts are now advising that the broad trend is looking up. “There is considerable momentum in the economy,” according to Neil Dutta at Renaissance Macro Research.

Here’s a graphical summary of how The Capital Spectator’s Q4 nowcast compares with recent history and forecasts from other sources:

Here are the individual nowcasts that are used to calculate CapitalSpectator.com’s median estimate:

As updated nowcasts 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 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.