"Taylored rules". Does one fit (or hide) all?

Modern monetary policymakers consider a huge amount of information to evaluate events and contingencies. Yet most research on monetary policy relies on simple instrument rules and one relevant underpinning for this choice is the good empirical fit of the Taylor rule. This paper challenges the solidness of this foundation. We investigate the way the coefficients of the Taylor-type rules change over time according to the evolution of general economic conditions. We model the Federal Reserve reaction function during the Greenspan’s tenure as a Logistic Smoothing Transition Regime model in which a series of economic meaningful transition variables drive the transition across monetary regimes. We argue that estimated linear rules are weighted averages of the actual rules working in the diverse monetary regimes, where the weights merely reflect the length and not necessarily the relevance of the regimes. Accordingly, an estimated linear Taylor-type reaction function tends to resemble the rule adopted in the longest regime. Thus, the actual presence of finer monetary policy regimes corrupts the general predictive and descriptive power of linear Taylor-type rules. These latter, by hiding the specific rules at work in the various finer regimes, lose utility directly with the uncertainty in the economy.