The “Taylor rule” prescribes a target Federal Funds rate based on the deviation of inflation and output from long-run means. The prescribed target rate is thus a function of the above parameters. However, according to number of financial economists (Taylor, 2007 & 2009; Kahn 2010) after the 2000 dotcom bubble bust Federal Reserve’s monetary policy deviated significantly from the target suggested by Taylor’s rule.

The following article explains Taylor’s Rule and some of the effect of monetary policy deviation on various aspects of US economy:

Kahn, George A. (2010), “Taylor Rule Deviations and Financial Imbalances,” Federal Reserve Bank of Kansas City Economic Review, Second Quarter, 63-99 (posted in Harvey)

The goal of this project is to apply Taylor Rule to the euro area. For the project purposes:

  1. Collect the US data for 1986-2021 (using sources described in the article, links provided below or any other reliable sources) and construct graphs similar to Chart 1 (Panel A &B) presented in the article for 1986-2021.
  2. Collect the Eurozone, Germany and Greece data for 1999 -2021 and construct the charts comparing Tylor rule recommended rates for the euro area, Germany and Greece to actual ECB monetary policy. Discuss the results.
  3. Read “A Comparison of Unconventional Monetary Policy in the U.S. and Europe” posted in Harvey, based on the reading and previous class discussion describe the similarities and differences in unconventional monetary policy in the U.S. and the euro area.
  4. Read The Economist articles “The euro enters its third decade in need of reform” and “The euro still needs fixing” (posted in Harvey) and discuss some challenges that usage of single currency presents and the consequences for the ECB monetary policy. Read additional current articles posted in Harvey and discuss the latest developments in ECB monetary policy and factors that impact euro and US dollar exchange rate.
  5. Specify the source(s) of your data (for each economic variable/indicator) and/or method of calculation. Include the table with the data used for creating your chart with your project.


Kahn, George A. (2010), “Taylor Rule Deviations and Financial Imbalances,” Federal Reserve Bank of Kansas City Economic Review, Second Quarter, 63-99.

Taylor, John B., 2007, Housing and Monetary Policy, Paper presented at a symposium sponsored by the Federal Reserve Bank of Kansas City, September, Jackson Hole.

Taylor, John B. 2009. “How Government Created the Financial Crisis.” Wall Street Journal, A19, February 9, 2009.

Recommended Sources of Data:

Use Economic projections – OECD Economic outlook – OECD Economic outlook Latest Edition – Economic Outlook No 111 – June 2022

(GDP output gap (Output gap as a percentage of potential GDP) and other indicators (such as Harmonized headline inflation) for Eurozone, Greece and Germany. It can be extended for 2000-2019 timeframe and Euro area data and other countries data can be added through “Customize” option)

Other potential sources of data    Bureau of Economic Analysis – GDP Congressional Budget Office – potential GDP U.S. Bureau of Labor Statistics – CPI FED Target rates  before 1990 FED target rates

The GDP gap or the output gap is the difference between actual GDP or actual output and potential GDP (output that can be sustained over the long term). The calculation for the output gap is Y–Y* where Y is actual output and Y* is potential output. If this calculation yields a positive number it is called an inflationary gap and indicates the growth of aggregate demand is outpacing the growth of aggregate supply—possibly creating inflation; if the calculation yields a negative number it is called a recessionary gap—possibly signifying deflation. The percentage GDP gap is the actual GDP minus the potential GDP divided by the potential GDP.

{(GDP_{actual} - GDP_{potential})}\over{GDP_{potential}}.

\log_b \!\left(\frac x y \right) = \log_b (x) - \log_b (y) \,

First difference of LOG = percentage change: When used in conjunction with differencing, logging converts absolute differences into relative (i.e., percentage) differences. Thus, the series DIFF(LOG(Y)) represents the percentage change in Y from period to period. Strictly speaking, the percentage change in Y at period t is defined as (Y(t)-Y(t-1))/Y(t-1), which is only approximately equal to LOG(Y(t)) – LOG(Y(t-1)), but the approximation is almost exactif the percentage change

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