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Posts tagged with: "A191RL1Q225SBEA"

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Nowcasting current activity

How's the economy

Forecasting, as we all know, tries to predict the future. For FRED’s purposes, that prediction is how a statistic will evolve. Nowcasting, a variant of forecasting, looks at the current state of a statistic that hasn’t yet been released because the period of coverage is not yet over. Nowcasting is one way to examine current economic activity; another was discussed in a previous post.

GDP is a popular target for nowcasting, and FRED covers the nowcasts of several Federal Reserve Banks—with the Federal Reserve Banks of Atlanta (GDPNow) and St. Louis nowcasts shown here along with the final GDP numbers released by the Bureau of Economic Analysis. To gaze into the future, focus on the very last data point for each nowcast (Q4 2017, shown here), as this is what nowcasting is all about.

The earlier data points for the nowcasts are the last estimates before the first (early) GDP release by the BEA, which is typically revised over time to create the green line. Like the BEA’s GDP numbers, the nowcasts are revised several times per month.

We see that there are disparities between the nowcasts. While they are in principle all based on the same information, estimates can differ because of different statistical methodologies and how they are revised over time. And what about the differences between the nowcasts and the final data? The BEA obviously has the advantage of access to more raw data and more time to refine the numbers.

How this graph was created: Search for “nowcast” and all the series you want should appear. Select the relevant series and click “Add to Graph.” From the “Edit Graph” menu, use the “Add Line” option to search for and select “real GDP” (use the growth rate series). Finally, start the graph in October 2011, the first data point of GDPNow.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: A191RL1Q225SBEA, GDPNOW, STLENI

Moderately well understood

The Great Moderation is evident, but its causes are complex

For decades, economists have puzzled over the reduction in macroeconomic volatility in the U.S. and world economies after the mid-1980s: In the last 25 years or so of the 20th century (highlighted by the blue bar in the graph), domestic output, employment, and inflation all fluctuated far less than they had in prior years. The data make the stabilization of the economy clear, yet economists haven’t reached consensus over the causes or duration of the Great Moderation.

The Federal Reserve points to monetary policy as a primary cause of the moderation, through orderly responses to inflation and GDP changes after the Great Inflation of the 1980s. Instead of waiting for the signs of economic recession or rising inflation to appear before acting, as was common in the “go-stop” practices of the 1960s and 1970s, the Federal Reserve began following a more systematic, rules-based approach. Another proposed cause is the structural change of the economy and the labor market. In the 1980s, labor patterns shifted away from manufacturing and toward less-volatile sectors. Also, new technology sped up communication and allowed producers to track inventory and demand more easily, leading to more stable output over time.

It’s easy to look to clear economic and policy changes as contributors to the Great Moderation, but there may be another factor at play: luck. Statistical models such as those proposed by Stock and Watson or Galí and Gambetti assert that, although some economic shocks did occur during the early part of the Great Moderation, they were less severe and better handled than those of the 1970s. While others dispute this claim, it still brings to mind the question: If luck is responsible, how long will it last?

There’s no answer yet to the question of whether the Great Moderation ended with the recession of 2007-09 or was merely temporarily disrupted by it. If we assume that structural and policy changes brought about the reduction in volatility, then, as long as those are maintained, the moderation ought to continue. However, if good fortune is responsible, we may still be waiting for the next shock that will bring about another increase in instability.

How this graph was created: Search “Real GDP” and select the relevant series. From the “Edit Graph” tab, click “Add Line” and select “Create use-defined line.” Set the date range from 1983 to 2017 and set both the start and end points as zero.

Suggested by Maria Hyrc and Christian Zimmermann.

View on FRED, series used in this post: A191RL1Q225SBEA

Diverging forecasts

Different stories from hard and soft data

Leading up to the 2017:Q1 GDP release, the two GDP tracking indicators in FRED have told starkly different stories of expected growth. These indicators, also referred to as Nowcast indicators, combine higher-frequency (e.g., monthly) economic data released before the GDP data to estimate growth in the current quarter. As shown in the graph above, in the beginning of April the GDPNow indicator from the Atlanta Fed forecasted a significant slow-down in growth, predicting 0.638 percent annualized growth in the first quarter. In contrast, the St. Louis Fed Economic News Index forecasted annualized GDP growth to be higher, at 2.89 percent in Q1.

What is driving the difference? An analysis into the data underlying the GDP trackers identifies stark differences between “hard” data and “soft” data in the first months of 2017. The Nowcasts rely on both soft data such as consumer and business surveys and hard data such as retail sales and industrial production. The GDPNow indicator uses more hard data, taking an accounting approach to building a forecast; the St. Louis Fed’s News Index is based more on soft data, which is surveyed from news reports. (For more insight on this topic, see this recent Economic Synopses essay.)

The graph below illustrates the contrast between a soft data series and a few hard data series over the beginning of 2017. The blue line is the University of Michigan Consumer Confidence Index, and the red and green lines are industrial production and retail sales, respectively. Each series is indexed to 100 at October 2016 to show the progression over the end of 2016 and beginning of 2017. Since October 2016, consumer confidence has risen dramatically while retail sales and industrial production have risen steadily but slowly. Soft data in 2017 have so far told a much more positive story of economic growth than hard data. One reasons analysts identify as a possible cause for the divergence between survey and hard economic data is consumer and business optimism after the election of Donald Trump that is not yet reflected in the hard data.

How these graphs were created: Top graph: Search for “GDPNow” in the FRED search box and graph the first series that is returned. Click the “Edit Graph” button and select the middle “Add Line” menu. First, search for “St. Louis Fed Nowcast” and add the St. Louis Economic News Index as a new line. Repeat this process for real GDP, selecting the series with units in percent change from preceding period at an annualized rate. Adjust the date range to 2015:Q3 to 2017:Q1. Bottom graph: Search for “Consumer Sentiment” in the FRED search box and graph the first series that is returned. Repeat the process above to add industrial production and retail and food services as additional lines on the graph. Once all three lines are added, select “Edit Line 1” on the “Edit Graph” menu and change the units to “Index.” Set the date to October 2016 and click “Copy to all.”

Suggested by Maximiliano Dvorkin and Hannah Shell.

View on FRED, series used in this post: A191RL1Q225SBEA, GDPNOW, INDPRO, RSAFS, STLENI, UMCSENT

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