Get 40% Off
🤯 This Tech Portfolio is up 29% YTD! Join Now to Get April’s Top PicksGet The Picks – Just 99 USD

Q2:2014 US GDP Nowcast: +3.2%

Published 07/28/2014, 06:48 AM
Updated 07/09/2023, 06:31 AM

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 2.9% decline for Q1 GDP (real seasonally adjusted annual rate), the US Bureau of Economic Analysis (BEA) is projected to report a 3.2% increase for its “advance” Q2 data on July 30. The current nowcast for Q2 is slightly below last month’s update that anticipated a 3.3% rise.

Meantime, The Capital Spectator’s current nowcast is slightly above the consensus forecast. Econoday.com reports that economists are looking for a 3.1% rise in Wednesday’s report—an increase that matches The Wall Street Journal’s 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:

US GDP: Actual vs Expectations Q2:2014

Next, let’s review the individual nowcasts that are used to calculate the median estimate:

U.S. GDP Q2 2014 Preview

As updated nowcasts are published, the chart below tracks the changes for context in assessing how the projections are evolving.

US GDP Udpated History

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.

Latest comments

Why not make it 5.9% growth, that sounds even better....
Risk Disclosure: Trading in financial instruments and/or cryptocurrencies involves high risks including the risk of losing some, or all, of your investment amount, and may not be suitable for all investors. Prices of cryptocurrencies are extremely volatile and may be affected by external factors such as financial, regulatory or political events. Trading on margin increases the financial risks.
Before deciding to trade in financial instrument or cryptocurrencies you should be fully informed of the risks and costs associated with trading the financial markets, carefully consider your investment objectives, level of experience, and risk appetite, and seek professional advice where needed.
Fusion Media would like to remind you that the data contained in this website is not necessarily real-time nor accurate. The data and prices on the website are not necessarily provided by any market or exchange, but may be provided by market makers, and so prices may not be accurate and may differ from the actual price at any given market, meaning prices are indicative and not appropriate for trading purposes. Fusion Media and any provider of the data contained in this website will not accept liability for any loss or damage as a result of your trading, or your reliance on the information contained within this website.
It is prohibited to use, store, reproduce, display, modify, transmit or distribute the data contained in this website without the explicit prior written permission of Fusion Media and/or the data provider. All intellectual property rights are reserved by the providers and/or the exchange providing the data contained in this website.
Fusion Media may be compensated by the advertisers that appear on the website, based on your interaction with the advertisements or advertisers.
© 2007-2024 - Fusion Media Limited. All Rights Reserved.