FRED lets you create commonly used data series that are not predefined. For example, you can normalize current account balances or government budget balances by GDP and you can deflate nominal data with a price index.

One popular variable that you can create is a bilateral real exchange rate index. While a nominal exchange rate is the relative price of 2 monies (e.g., the relative price of a euro in terms of U.S. dollars), a real exchange rate is the relative price of consumption baskets in two countries. A consumption basket is a set of goods and services that represent the purchases of a typical consumer in country in a given year. Thus the real exchange rate is the price of European goods in terms of U.S. goods. One converts a nominal exchange rate into a real rate by multiplying by the ratio of the national price levels:

U.S. goods per euro area goods basket = (USD per euro) * (euro price level) / (U.S. price level)

FRED has many kinds of broad, real effective exchange rates. Here is a list of FRED’s real U.S. exchange rates. These are effective or trade-weighted real exchange rates. They are weighted averages of bilateral real exchange rates. Currencies of the countries that the U.S. trade with the most receive the highest weights in the formula. Real effective exchange rates provide a look at changes in the overall value of foreign consumption baskets in terms of the U.S. consumption basket. When a real effective exchange rate rises (falls), the average foreign consumption basket becomes more (less) expensive in terms of U.S. consumption.

But what if you want to see the price of foreign consumption in terms of U.S. consumption for a particular country or area? For example, what if you want to see the real exchange rate for the dollar per euro, as we detailed at the start of the post? You can construct such a bilateral real exchange rate yourself in FRED using monthly price and exchange rate data from the U.S. and the euro area. The following instructions give you the graph at the top of this post.

1. Search for “euro” in the FRED search box and select “U.S. / Euro Foreign Exchange Rate.” The default graph will be a daily exchange rate (DEXUSEU).

2. Because consumer price series are monthly (or quarterly), use the orange “Edit Graph” button on the right hand side to change the frequency to monthly and the aggregation method to “average.” This series is series “a” in the graph. Keep the editing box open.

3. Add the U.S. and euro area CPI series using the “customize data” area.

a. To add the U.S. CPI data, type “cpi” directly under the text that says “You can begin by adding a series to combine with your existing series.” Click on the first series in the popup list called “Consumer price index for all urban consumers.” Click “Add” on the right-hand side of the box and the U.S. CPI series became series “b” in the graph. Note that the graph itself has not changed.

b. To add the euro area CPI data, type “euro cpi” directly under the text that says “You can begin by adding a series to combine with your existing series.” Click on the first series in the popup list called “Harmonized Index of Consumer Prices: All Items for the Euro Area.” Click “Add” on the right-hand side of the box and the euro CPI series became series “c” in the graph.

4. Now that we have defined our exchange rate and price series, we use them to construct a real exchange rate by typing the following formula in the “Formula” box near the bottom of the editing box: a*c/b. Then click “Apply” to the right of the box. The picture in the graph will finally change to the bilateral real exchange rate, i.e., baskets of U.S. goods per basket of euro area goods.

5. To change the real exchange rate to an index, select “Index (Scale value to 100 for chosen date)” from the “Units” box at the bottom of the editing box and then type “1999-01-01” in the date box. Close the edit box with the X in the upper right-hand corner.

6. To see a long span of the data series that you have created, select “Max” from the data range choices, i.e., “1Y | 5Y | 10Y | Max”, at the top of the graph.

Suggested by Chris Neely.