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The FRED® Blog

Fan your forecasting flame with FREDcast FRED’s new forecasting game

On January 20th FRED’s newest data gizmo, FREDcast, is coming out of beta. FREDcast is an interactive forecasting game that allows users to enter forecasts for four different economic variables, track their forecast’s accuracy on the scoreboards, and compete with friends and other users in leagues. The game is designed for all levels of users, from high school students to professional forecasters. Just log-in to FREDcast using your FRED account and walk through the prompts to enter your forecasts for each variable. FREDcast forecasts are zero horizon, meaning users forecast economic data for the month (or quarter) in which they are in. For example, from January 1st to January 20th, users submit forecasts for the January unemployment rate, the January consumer price index (CPI), the January payroll employment, and quarter one real gross domestic product (GDP). Forecasts are due by the 20th of each month, and scores are released as the economic data come out. View exact release dates on FRED’s economic calendar.

The four FREDcast series are available in FRED. Below is a graph of each series in the appropriate units for FREDcast forecasts. All series in FREDcast are seasonally adjusted. From top to bottom: Real gross domestic product (GDP) is the only quarterly series, and the units are the percent change from the preceding period at a seasonally adjusted annual rate. Next is the unemployment rate, which is forecast as a monthly rate. Next are the consumer price index (CPI) and payroll employment. The inflation series used in FREDcast is the percent change in the CPI from one year ago, while payroll employment is the level change from the prior month measured in persons.

How these graphs were created: GDP: Search for real gross domestic product, and graph the series with the units “Percent Change from Preceding Period, Quarterly, Seasonally Adjusted Annual Rate.” Set the start date to 2006-07-01, and follow this path: Edit Graph > Format > Graph Type > Bar. Unemployment Rate: Search for unemployment rate, and graph the seasonally adjusted civilian unemployment rate. Set the start date to 2006-12-01. CPI: Search for consumer price index, and graph the series “Consumer Price Index for All Urban Consumers: All Items” with monthly, seasonally adjusted units. Set the start date to 2006-11-01, and follow this path: Edit Graph > Units > Percent Change from Year Ago. Payroll Employment: Search for payroll employment, and graph the series “All Employees: Total Nonfarm Payrolls” in seasonally adjusted units. Set the start date to 2006-12-01, and follow this path: Edit Graph > Units > Change, Thousands of Persons. Last, multiply the series by 1000 to get it in units of persons by entering a*1000 in the formula box and clicking “Apply.”

Suggested by Michael Owyang and Hannah Shell.

View on FRED, series used in this post: A191RL1Q225SBEA, CPIAUCSL, PAYEMS, UNRATE

Taxing couples A history of tax exemptions for couples

FRED’s recent addition of data from the Internal Revenue Service is a gold mine of interesting factoids. The data cover different tax returns and drill down to particular line items. There are even time series on amounts for exemptions, deductions, and credits. The graph shows one such exemption, the personal exemption for married couples, in three versions: the nominal value as written in the tax code (blue), the real value after adjusting for inflation using the consumer price index (red), and the real value adjusting for the nominal increase in incomes using personal income per capita (green).

Move the glider to look at different time periods and you’ll notice the exemption was quite high (even in nominal terms) in the first years after the income tax was introduced, which is one factor explaining why only a minority of households were paying any tax in the first years. That eroded substantially after WWII, when the exemption was small. It has increased recently in nominal terms and keeps up with inflation but not with the increase in incomes. Indeed, it’s now trending, in real terms as deflated by income, to the lowest it has ever been. In terms of 1982-84 prices, it’s now at about $2300, compared with about $2000 at its lowest point.

How this graph was created: The exemption is among the most popular in the data release, so click on it and you have the blue line. From “Edit Graph,” use the add line feature to search for the same exemption and add to the line CPI (using the longer series) and apply formula a/b*100. Again add a line with the same exemption, add to it personal income per capita (make sure not to use the real series) and apply formula a/b*14000 (with 14000 being the factor needed to make the line roughly match the $2000 exemption in 1982-84, which is the base year for the CPI).

Suggested by Christian Zimmermann.

View on FRED, series used in this post: A792RC0A052NBEA, CPIAUCNS, IITPEMC

Regional income

In our previous blog post, we reported that inflation was higher in the New York City area than in the Cleveland area. Today, we look at their local incomes to see if the same conditions apply. The top graph shows personal income per capita for these areas. It’s not possible to get a perfect geographic match, but we use Cuyahoga County to represent the Cleveland consolidated metropolitan statistical area and a broader area around New York City (but without the western Connecticut towns used in the previous post). Without forgetting these mismatches, we see that overall income growth seems to be similar in both areas, except for the bubble in NYC just before the previous recession. So it doesn’t look like New Yorkers are compensated for their more-quickly-increasing living expenses.

The bottom graph reveals a similar exercise but with median household income, which is collected by county. Again, we use Cuyahoga County as a proxy for the Cleveland MSA. We use New York County to represent the NYC area.* The picture looks quite different, with Manhattan residents showing impressive income gains recently that probably aren’t matched in the wider New York metropolitan area.

*Admittedly, this is a restrictive choice; but piecing together data from the many surrounding counties is beyond the scope of this post. Readers are always encouraged to browse through FRED’s data aisles to find the best data to meet their needs.

How these graphs were created: Search for “personal income per capita Cleveland” and Cuyahoga County should be near the top. From the “Edit Graph” button, add a line searching for “personal income per capita New York.” For both lines, change units to “100 for selected date” using 1984-01-01 to match the data of the previous blog post. For the second graph, search for “median income Cleveland” then add the second line by searching for “median income New York county.”

Suggested by Christian Zimmermann.

View on FRED, series used in this post: MHINY36061A052NCEN, MHIOH39035A052NCEN, NEWY636PCPI, PCPI39035

Regional inflation

If everyone uses the same currency in the United States, shouldn’t prices and inflation rates be nearly uniform across the nation? To help answer this question, the U.S. Bureau of Labor Statistics computes a limited set of consumer price indices for consolidated metropolitan statistical areas (that is, the agglomeration of neighboring MSAs). They don’t provide as much detail as the nationwide CPI and they’re not necessarily available at monthly intervals, due to data sampling issues. But they can be revealing.

The graph above compares Cleveland and New York City: NYC prices seem to be climbing more than those in northern Ohio. Note that this doesn’t say anything about the levels, only the evolutions. But is this inflation differential uniform across goods? The graph below eliminates shelter from the mix and the gap between the two is noticeably smaller. In other words, differences in inflation for a refrigerator or a gallon of milk are much smaller across the country than differences in inflation for housing.

How these graphs were created: Search for “CPI CMSA Cleveland” and click on the monthly series. From the “Edit Graph” section / “Add Line” tab, search for “CPI CMSA NY” and select the monthly series. In the “Format Graph” section, change the mark type to “square” (there are marks for Cleveland because data are not available for every month). Finally, change the sample period to start on 1984-01-01. For the second graph, repeat the procedure by adding “shelter” to the search terms.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: CUURA101SA0L2, CUURA210SA0, CUURA210SA0L2, CUUSA101SA0

Moving in your neighborhood County-level net migration data

FRED has recently added county-level data on net migration based on the American Community Survey. This survey asks whether the respondent has lived elsewhere in the previous year, and the responses are then extrapolated to represent migration flows across the U.S. The map shows the last year for which these data are available. What’s interesting about this picture is that there is no clear pattern. One could have expected, say, a hollowing out of the Midwest in favor of the coasts, but this does not appear to be the case. More-detailed analysis would likely illuminate some deeper patterns, but it looks like migration is largely regional in nature: Moves between neighboring counties, for example, create this random-looking patchwork.

How this graph was created: On GeoFRED, open the cogwheel, select geography “County” and search for “Migration.”

Suggested by Christian Zimmermann.



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