So, do you think we might be in a recession today (Part II)?

Look around for enough indicators (for example, car mileage) and you might think so. Addition to this mail. Let’s see what regular and high frequency indicators say.

Here are the key indicators tracked by the NBER Business Cycle Dating Committee:

Figure 1: Nonfarm payrolls (dark blue), June 25 Bloomberg NFP Consensus (ble+), industrial production (red), 2012 personal income excluding transfers, USD (green), manufacturing and trade sales in 2012 dollars (black), consumption in Ch.2012 $ (light blue) and monthly GDP in Ch.2012 $ (pink), all logs normalized to 2020M02 = 0. NBER recession dates from peak to a minimum, shaded in grey. Source: BLS, Federal Reserve, BEA, via FRED, IHS Markit (née Macroeconomic Advisors) (issue 06/01/2022), NBER and author’s calculations.

Another indicator is the Chicago Fed’s National Activity Index (CFNAI). Here is the CFNAI-3MA (3 month moving average):

From the website of the Federal Reserve Bank of Chicago with a description of the index:

CFNAI is a weighted average 85 existing monthly indicators national economic activity. It is built to have a mean of zero and a standard deviation of one. Since economic activity tends to grow over time, a positive index value corresponds to growth above the trend, and a negative index value corresponds to growth below the trend.

The 85 economic indicators included in the CFNAI are based on four broad categories of data: production and income; employment, unemployment and working hours; personal consumption and housing; and sales, orders and stocks. Each of these data series measures some aspect of overall macroeconomic activity. The derived index provides a single aggregate measure of a factor common to these national economic data.

Federal Reserve Bank of Chicago notes:

After a period of economic recovery, an increased likelihood of a recession has historically been associated with a CFNAI-MA3 value below -0.70. Conversely, after a period of economic downturn, an increasing upside probability has historically been associated with a CFNAI-MA3 value above -0.70, and a significant upside probability has historically been associated with a CFNAI-MA3 value above +0.20.

The CFNAI-3MA value for May was +0.20.

True, these are retrospective indicators related to May conditions (at best).

What about indicators with a weekly frequency? Here is the Lewis-Mertens-Stock measure based on data up to June 18th.

Source: FREDas of 06/25/2022.

Here is a description of this index:

WEI is an index of real economic activity using timely and relevant high-frequency data. It is a common component of ten different daily and weekly series covering consumer behavior, labor market and production. WEI scales with four-quarter GDP growth; for example, if the WEI shows -2 percent and the current level of the WEI is maintained throughout the quarter, one would expect the average GDP for that quarter to be 2 percent lower than a year earlier.

WEI is a composite of 10 weekly economic indicators: Same Store Redbook Sales, Rasmussen Consumer Index, New Jobless Claims, Ongoing Jobless Claims, Adjusted Income/Employment Tax Withholding (from Booth Financial Consulting) , rail transportation from the Association of American Railroads), the American Personnel Association Workforce Index, steel production, wholesale sales of gasoline, diesel and jet fuel, and the average weekly electricity load in the United States (other data provided by Haver Analytics). All series are presented as percentage changes from last year. These series are combined into a single index of weekly economic activity.

For more information, including an analysis of model performance, see Lewis, Mertens, and Stock (2020), “U.S. Economic Activity in the Early Weeks of the SARS-Cov-2 Outbreak.”

Here is the OECD Weekly Tracker for the US pertaining to data up to June 12:

Source: OECDas of 06/25/2022.

Description part:

Weather forecast with Google Trends

Signals about various aspects of the economy from Google Trends are extracted and aggregated using machine learning to provide a timely picture of the macro economy. The algorithm extracts and compiles information about consumption (for example, from search queries “cars”, “household appliances”), labor markets (for example, “unemployment benefits”), housing (for example, “real estate agency”, “mortgage”), business -services (e.g. “venture capital”, “bankruptcy”), industrial activities (e.g. “marine transport”, “agricultural equipment”), trade (e.g. , “recession”) and poverty (for example, “food bank”). The use of multiple variables reduces the risk associated with structural abnormalities in specific runs, as highlighted by the failure of the Google Flu experiment.

The weekly tracker uses a two-stage model to predict weekly GDP growth based on Google Trends data. First, the quarterly GDP growth model is estimated based on Google Trends search intensity on a quarterly basis. Second, the relationship between Google Trends and activity, using the same elasticities estimated from the quarterly model, is applied to the weekly Google Trends series to produce a weekly tracker. Thus, the OECD Weekly Tracker can be interpreted as an estimate of the annual growth rate of “weekly GDP” (in the same week compared to the previous year).

What about other big data indexes? Here is the Brave-Butters-Kelley (500 time series) matched big data index for April (May is indexed at the end of the month):

Source: FREDas of 06/25/2022.

This index is described as follows:

The Brave-Butters-Kelly Indices (BBKI) is a research project of the Federal Reserve Bank of Chicago. The BBK Coincidence and Leading Indices and monthly GDP growth for the US are constructed from a folded dynamic factor analysis of a panel of 500 monthly measures of real economic activity and quarterly real GDP growth.

The BBK Coincidence Index is the sum of leading and lagging cycle subcomponents, measured in standard deviation units from the real GDP growth trend.

As of April, the index was above the trend, so no negative growth.

It would be nice to have more recent big data observations. Here is Google’s weekly mobility indicator:

Source: Slok, Agarwal, Shah, Deceleration clockApollo (June 22, 2022), offline.

As to whether the VMT slowdown signals a recession (as suggested by mr. Kopitz), well, it’s hard to say. Here’s a picture that suggests gasoline prices may have something to do with lower VMT (keeping in mind that inflation is not the same as a recession).

Figure 2: Quarterly annual growth rates of vehicle mileage (VMT) (blue, left scale) and real gasoline price (yellow, right logarithmic scale). Gasoline prices are deflated by the core consumer price index. Peak-to-trough dates as determined by the NBER are in grey. Source: DoT, EIA, BLS via FRED, NBER and author’s calculations.

I estimate the price elasticity of VMT in the short run to be about -0.19 (using the first specification of log differences for the 2000-2022 sample period, including consumption as an activity variable and covid variable, with a price coefficient significantly different from zero in the period samples). 95% level.

All this data tells me that as of May we have not had a recession. And as of June, we are also unlikely to have a recession, although the fact that the data revision is a reality makes me a little cautious. Will we be in a recession next year, well, there are more alarming indicators.