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Are we still in a recession?

What to expect from the NBER business cycle dating committee

The Downturn and Rebound

  • April 29, 2020: In its advance estimate, the Bureau of Economic Analysis (BEA) reported that real GDP for the first quarter of 2020 fell at a 4.8% annual rate.
  • May 8, 2020: The Bureau of Labor Statistics reported that nonfarm payrolls fell by 20.5 million in April—the largest one-month percentage decline on record (dating back to 1939).
  • June 8, 2020: The National Bureau of Economic Research Business Cycle Dating Committee (NBER BCDC) announced that the 128-month expansion (the longest in U.S. economic history, dating back to 1854) ended sometime in February 2020.
  • Since then, the U.S. economy has rebounded sharply, posting large increases in real GDP and nonfarm payroll employment and a large decline in the unemployment rate. But the NBER BCDC hasn’t yet announced an end to the recession…

The BCDC’s Methods

The BCDC patiently assesses business cycle peaks and troughs. For example, they announced that the trough of the 2007-2009 recession occurred in June 2009 only on September 20, 2010—which is a lag of 15 months.

The BCDC also emphasizes economywide economic indicators. In their view, dating peaks and troughs is best accomplished by looking at measures of activity that cut across all sectors of the economy, rather than a small number of key sectors (such as the Federal Reserve’s industrial production index, which measures output produced by the nation’s manufacturers, utilities, and mining industry). In their June 8 announcement, the BCDC indicated that real GDP and real gross domestic income (GDI) are the two “most reliable” comprehensive measures of economic activity.

The FRED graph above shows real GDP data from the BEA: Real GDP fell in the first and second quarters of 2020, but then rebounded in the third quarter. However, unlike real GDP, real GDI isn’t yet available for the third quarter because the BEA hasn’t yet reported corporate profits—a key component of GDI. Corporate profits will be reported in the second estimate of GDP, scheduled for release on November 25.

What Else Do They Look At?

Over time, the BCDC has examined several comprehensive monthly indicators, such as real manufacturing and trade sales, nonfarm payroll employment, and civilian employment. In its September 2020 announcement, the BCDC emphasized that real personal consumption expenditures and real personal income excluding current transfer payments are the two broadest measures of aggregate expenditures and aggregate income. Use this FRED dashboard to follow such comprehensive indicators. April 2020 was the trough month of all five of these indicators. Moreover, each of the indicators has since risen sharply, consistent with the increase in real GDP.

What’s Different This Time?

As noted above, the BCDC generally prefers to wait until there’s conclusive evidence that the economy has transitioned from a period of recovery to expansion. The unique features of this pandemic-spawned recession have, as the BCDC noted on June 8, 2020, “resulted in a downturn with different characteristics and dynamics than prior recessions.” So, while the data suggest that an economywide trough in economic activity occurred sometime in the spring, the pandemic remains a key driver of economic policy and the behavior of many governments and individuals worldwide. From that standpoint, the length and strength of the recovery is uncertain.

How this graph was created: Search FRED for real GDP, select the series, and start the sample period on 2014-01-01.

Suggested by Kevin Kliesen.

View on FRED, series used in this post: GDPC1

The state of the economy, weekly

Measuring the condition of an economy isn’t easy. The most reliable indicators are computed and released only quarterly or yearly, and then with a considerable lag. They are also subject to revisions. For a policymaker or anyone needing to observe and assess the economy, this can be very frustrating.

Fortunately, FRED provides access to some series that have higher frequency (weekly or even daily), are released faster, and don’t need revisions. Individually, these components offer only a partial picture of the economy; but together, they may be informative.

The Lewis-Mertens-Stock index shown in the FRED graph above provides this kind of informative picture of the economy: It comprises ten daily or weekly series, uses a statistical technique called factor analysis to determine what’s common among them, and scales the result so it can be presented as a percentage change of GDP from exactly one year ago. (That is, since the same day in the previous year.)

Daily GDP is obviously not known, so the index can’t be compared with any actual daily GDP reading. But it can be compared with quarterly GDP, which we do in the graph below. As shown by the tightness of the two lines, the index performs remarkably well.

How these graph were created: Search FRED for “weekly index” and the series should be among the top choices. From this first graph, use the “Edit Graph” panel to change frequency to quarterly (using the average). Open the “Add Line” tab, search for “real GDP,” select it, and change units to “Percentage change from year ago.” Change sample period to start when both series are available.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: GDPC1, WEI

A lesson on time series to get you started with FREDcast

Learn how to go from zero to forecasting hero

Two years ago, the St. Louis Fed introduced FREDcast, a forecasting game in the style of fantasy sports. In FREDcast, users enter a forecast for four economic time series each month: GDP, payroll employment, the unemployment rate, and CPI. Now in its third year, FREDcast is growing in popularity and taking hold at some major universities.

The motivation behind FREDcast has been to lower the barriers of forecasting macroeconomic time series, and the game is designed so that anyone with a basic understanding of data can join the game and start forecasting. One of the major challenges, though, is establishing that common, basic understanding of time series data. So let’s lay out a few concepts and definitions to get you started.

What is a time series?

In plain English, a time series is a sequence of data observations collected over time. For example, a collection of the daily closing values of the Dow Jones Industrial Average is a time series. Time series data differ from cross-sectional data, which are observed over many subjects either at the same time or where time is not a factor. Test scores from a statistics class mid-term would be an example of a cross-sectional dataset.

The FRED graph above plots the level values of a time series: real gross domestic product (GDP). Real GDP is the value of an economy’s production of goods and services—a prominent economic variable. In FREDcast, users forecast the growth rate of real GDP, but for illustration purposes we’ll look at the levels here. On the horizontal axis are the quarters of the year when GDP is measured, and on the vertical axis are the values of GDP collected in those quarters. The units of the series are listed on the vertical axis, so we can see that GDP here is measured in billions of 2012 dollars.

The data show some patterns. The most striking pattern is that the value of GDP increases. A secondary pattern involves the smaller movements in GDP up and down. These patterns represent two of the four core properties of time series data:

1. The trend. In any time series, the trend is the slow change in the series over a long period. In this case, it’s a slow increase over time. In the natural world, this is analogous to, say, climate change: how temperatures have been rising slowly over time.

2. The cycle. In most economic forecasting and in FREDcast, we’re interested in the growth rate of a variable. Growth rates can be calculated in a variety of ways, but the core idea is to look at the change in the value of the series from one period to another. As these periods are typically close together (e.g., month to month or year to year), the growth rate captures the smaller, short-term movements in the series instead of the long-term trend movements. These short-term movements are called the cycle, which represents predictable increases and decreases in the data that occur in sync with another process (e.g., the business cycle). In the graph above, the smaller movements in the data occur in sync with the recessions (gray shading). To continue with our weather analogy, cycles could be thought of as heating and cooling in the atmosphere that result in distinct periods of both fair weather and storms.

3. Seasonality. There are cyclical patterns that repeat over units of time (e.g., daily, weekly, monthly). Think about the weather in general in your hometown for an entire year: The temperature displays the same basic seasonal pattern, right? GDP does have a seasonal pattern, but the agency that collects GDP )the Bureau of Economic Analysis) removes the seasonal pattern before publishing the data. For most economic data, we care about the trend and the cycle but not the seasonal variation, since that represents patterns in the data that are independent of overall economic health.

4. Random variation. The last property of time series data is random variation. Not every part of a time series can be explained by a trend, cycle, or seasonal pattern. What’s left over are just random movements that can’t be predicted. Consider, say, a chilly day in mid-July or a warm sunny day in the dead of winter.

What are the time series in FREDcast?

With the exception of the unemployment rate, all the variables used in FREDcast are either changes or growth rates. The variables have also been seasonally adjusted to remove seasonal variation. The leftover components in the FREDcast variables, then, are the cycle and the random variation. Because we can’t predict random variation, FREDcast forecasts are really focused on forecasting the cycle of the four variables.

A simple method to start forecasting is to plot the growth rate of the FREDcast variable* and look at how the cycle relates to the business cycle. Forecasters should then consider the context of the overall economy when making forecasts and draw on their economic knowledge to make a prediction.

Time series data may seem like an unapproachable subject to a new student of economics, but we hope short, simple lessons like these can help anyone become more comfortable with the data.

How this graph was created. Search for “real GDP” on FRED, select the first result, and click “Add to Graph.”

*FREDcast players forecast CPI inflation, for example. Now, CPI is the consumer price index, which, as the name indicates, is an index. But a more common way to talk about inflation is as a growth rate. From the CPI series page in FRED, go to the “Edit Graph” panel and then to the “Units” menu: Change the units from “Index 1982-1984=100” to “Percent Change from Year Ago” and you have a growth rate.

Suggested by Diego Mendez-Carbajo and Hannah Shell.

View on FRED, series used in this post: GDPC1

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