10 Federal Reserve Bank of Richmond Economic Quarterly
King, and Wolman find, in the context of a stylized model calibrated to match
aspects of money demand and price dynamics in the postwar United States,
that the optimal rate of inflation is −0.76 percent per year. By comparison, in
their model the Friedman rule is associated with a deflation rate of 2.93 percent
per year. Thus, in the study by Khan, King, and Wolman, the optimal policy
is closer to price stability than to the Friedman rule. Taking these numbers at
face value, one might conclude that price stickiness is the dominant friction
in shaping optimal monetary policy. However, Khan, King, and Wolman
(2003) and Schmitt-Groh´e and Uribe (2004a, 2007b) show that the resolution
of the tradeoff is quite sensitive to plausible changes in the values taken by
the structural parameters of the model.
In Schmitt-Groh´e and Uribe (2007b), we find that a striking character-
istic of the optimal monetary regime is the high sensitivity of the welfare-
maximizing rate of inflation with respect to the parameter governing the de-
gree of price stickiness for the range of values of this parameter that is em-
pirically relevant. The model underlying the analysis of Schmitt-Groh´e and
Uribe (2007b) is a medium-scale model of the U.S. economy featuring, in
addition to money demand by households and sticky product prices, a number
of real and nominal rigidities including wage stickiness, a demand for money
by firms, habit formation, capital accumulation, variable capacity utilization,
and investment adjustment costs. The structural parameters of the model are
assigned values that are consistent with full- as well as limited-information
approaches to estimating this particular model.
In the Schmitt-Groh´e and Uribe (2007b) model, the degree of price stick-
iness is captured by a parameter denoted α, measuring the probability that a
firm is not able to optimally set the price it charges in a particular quarter. The
average number of periods elapsed between two consecutive optimal price ad-
justments is given by 1/(1 − α). Available empirical estimates of the degree
of price rigidity using macroeconomic data vary from two to five quarters, or
α ∈ [0.5, 0.8]. For example, Christiano, Eichenbaum, and Evans (2005) esti-
mate α to be 0.6. By contrast, Altig et al. (2005) estimate a marginal-cost-gap
coefficient in the Phillips curve that is consistent with a value of α of around
0.8. Both Christiano, Eichenbaum and Evans (2005) and Altig et al. (2005)
use an impulse-response matching technique to estimate the price-stickiness
parameter α. Bayesian estimates of this parameter include Del Negro et al.
(2004), Levin et al. (2006), and Smets and Wouters (2007), who report pos-
terior means of 0.67, 0.83, and 0.66, respectively, and 90 percent posterior
probability intervals of (0.51,0.83), (0.81,0.86), and (0.56,0.74), respectively.
Recent empirical studies have documented the frequency of price changes
using microdata underlying the construction of the U.S. consumer price index.
These studies differ in the sample period considered, in the disaggregation
of the price data, and in the treatment of sales and stockouts. The median
frequency of price changes reported by Bils and Klenow (2004) is four to five