US economic growth will slow in this year’s first quarter, according to a range of projections. The consensus view via the Wall Street Journal’s April survey of economists anticipates a 1.5% annualized gain for real GDP in the first three months of this year—a substantially lower pace than the reported 2.6% rate for last year’s fourth quarter. The Capital Spectator’s revised median nowcast for Q1:2014 GDP also reflects a slowdown, but a considerably milder one vs. the crowd’s outlook. In fact, today’s updated outlook for 2.4% (real seasonally adjusted annual rate) is modestly above the previous 2.0% nowcast that was published here on March 25. What’s changed? The economic numbers have improved since our last update.
Predictions come with all the usual caveats, of course, but the latest rise in The Capital Spectator’s nowcast suggests that Wednesday’s advance Q1 estimate from the US Bureau of Economic Analysis could deliver stronger growth than economists are anticipating. But estimating this week’s GDP report comes with a higher dose of uncertainty because of the slippery challenge of trying to factor in the effects of a harsh winter in January and February. Several key corners of the economy certainly look resilient, based on the latest numbers. Consumer spending and business investment in particular had a decent first quarter. But some analysts warn that there are some trouble spots as well. The economists at First Trust explain it this way:
The problem, at least as far as Q1 is concerned, is that the parts of GDP where we have less information look downright ugly. Government purchases, international trade, and inventories are set to be major drags on the economy in Q1. For some reason, these are the areas hit by the weather. As a result, our “add-em up” calculations suggest real GDP grew at only a 0.5% annual rate.
Whatever the Q1 numbers reveal, most analysts think that any deceleration in growth will be temporary. The Wall Street Journal survey advises that economists see growth picking up to a 3.0% rate for Q2.
Meantime, here’s how The Capital Spectator’s revised Q1 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:
Here’s how the Q1:2014 nowcast updates compare so far:
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 (S&P 500), 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 anautoregressive 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.
This piece is cross-posted from The Capital Spectator with permission.