We prove that the ratio of kurtosis to the frequency of price changes is a sufficient statistic for the real effects of monetary shocks, measured by the cumulated output response following the shock. The sufficient statistic result holds in a large class of models which includes Taylor (1980); Calvo (1983); Reis (2006 ); Golosov and Lucas (2007 ); Nakamura and Steinsson (2010); Midrigan (2011); and Alvarez and Lippi (2014). Several models in this class are able to account for the positive excess kurtosis of the size distribution of price changes that appears in the data. We review empirical measures of kurtosis and frequency and conclude that a model that successfully matches the microevidence on kurtosis and frequency produces real effects that are about four times larger than in the Golosov-Lucas model, and about 30 percent below those of the Calvo model. We discuss the robustness of our results to changes in the setup, including small inflation and leptokurtic cost shocks. (JEL, E23, E31)

If Exchange Rates are Random Walks, Then Almost Everything We Say About Monetary Policy is Wrong by Fernando Alvarez, Andrew Atkeson and Patrick J. Kehoe. Published in volume 97, issue 2, pages 339-345 of American Economic Review, May 2007

We analyze the effects of money injections on interest rates and exchange rates when agents must pay a Baumol‐Tobin‐style fixed cost to exchange bonds and money. Asset markets are endogenously segmented because this fixed cost leads agents to trade bonds and money infrequently. When the government injects money through an open market operation, only those agents that are currently trading absorb these injections. Through their impact on these agents’ consumption, these money injections affect real interest rates and real exchange rates. The model generates the observed negative relation between expected inflation and real interest rates as well as persistent liquidity effects in interest rates and volatile and persistent exchange rates.

We study models where prices respond slowly to shocks because firms are rationally inattentive. Producers must pay a cost to observe the determinants of the current profit maximizing price, and hence observe them infrequently. To generate large real effects of monetary shocks in such a model the time between observations must be long and/or highly volatile. Previous work on rational inattentiveness has allowed for observation intervals that are either constant-but-long (e.g. Caballero, 1989 or Reis, 2006) or volatile-but-short (e.g. Reis's, 2006 example where observation costs are negligible), but not both. In these models, the real effects of monetary policy are small for realistic values of the duration between observations. We show that non-negligible observation costs produce both of these effects: intervals between observations are infrequent and volatile. This generates large real effects of monetary policy for realistic values of the average time between observations.

Abstract We give a full analytic characterization of a large class of sticky-price models where the firm’s price-setting behavior is described by a generalized hazard function. Such a function allows for a vast variety of empirical hazards to be fitted. This setup is microfounded by random adjustment costs, as in Caballero and Engel (1999), or by information frictions, as in Woodford (2009). We establish two main results. First, we show how to identify all the primitives of the model, including the distribution of the fundamental adjustment costs and the implied generalized hazard function, using the distribution of price changes. Second, we derive a sufficient statistic for the aggregate effect of a monetary shock: given an arbitrary generalized hazard function, the cumulative impulse response of output to a once-and-for-all monetary shock is proportional to the ratio of the kurtosis of the steady-state distribution of price changes over the frequency of price adjustment. We prove that Calvo’s model yields the upper bound and Golosov and Lucas’s model the lower bound on this measure in the class of random menu cost models.

We study the propagation of monetary shocks in a sticky‐price general equilibrium economy where the firms' pricing strategy features a complementarity with the decisions of other firms. In a dynamic equilibrium, the firm's price‐setting decisions depend on aggregates, which in turn depend on the firms' decisions. We cast this fixed‐point problem as a Mean Field Game and prove several analytic results. We establish existence and uniqueness of the equilibrium and characterize the impulse response function (IRF) of output following an aggregate shock. We prove that strategic complementarities make the IRF larger at each horizon. We establish that complementarities may give rise to an IRF with a hump‐shaped profile. As the complementarity becomes large enough, the IRF diverges, and at a critical point there is no equilibrium. Finally, we show that the amplification effect of the strategic interactions is similar across models: the Calvo model and the Golosov–Lucas model display a comparable amplification, in spite of the fact that the non‐neutrality in Calvo is much larger.

Abstract In a broad class of sticky-price models, the non-neutrality of nominal shocks is captured by a simple sufficient statistic: the ratio of the kurtosis of the price change distribution over the frequency of price changes. We test the sufficient statistic proposition using data for a large sample of products representative of the French economy. We first extend the theory to allow for empirically relevant monetary shocks with a transitory predictable component. We then use the microdata to measure kurtosis and frequency for about 120 producer price indices industries and 220 consumer price indices categories. We use a Factor-Augmented Vector Autoregressive (FAVAR) model to measure the industries’ response to monetary shocks, under alternative identification schemes. The estimated degree of non-neutrality correlates with the kurtosis and the frequency consistently with the predictions of the theory. Several robustness checks are discussed.