Abstract We establish five facts about prices in the U.S. economy: 1) The median frequency of nonsale price change is 9-12% per month, roughly half of what it is including sales. This implies an uncensored median duration of regular prices of 8-11 months. Product turnover plays an important role in truncating price spells in durable goods. The median frequency of price change for finished goods producer prices is roughly 11% per month. 2) One-third of regular price changes are price decreases. 3) The frequency of price increases covaries strongly with inflation while the frequency of price decreases and the size of price increases and price decreases do not. 4) The frequency of price change is highly seasonal: It is highest in the 1st quarter and lowest in the 4th quarter. 5) The hazard function of price changes for individual consumer and producer goods is downward sloping for the first few months and then flat (except for a large spike at 12 months in consumer services and all producer prices). These facts are based on CPI microdata and a new comprehensive data set of microdata on producer prices that we construct from raw production files underlying the PPI. We show that the 1st, 2nd and 3rd facts are consistent with a benchmark menu-cost model, while the 4th and 5th facts are not.

This chapter examines the concept of inflation persistence in macroeconomic theory. It begins with a definition of persistence, emphasizing the difference between reduced-form and structural persistence. It then examines a number of empirical measures of reduced-form persistence, considering the possibility that persistence has changed over time. The chapter then examines the theoretical sources of persistence, distinguishing “intrinsic ” from “inherited” persistence, and deriving a number of analytical results on persistence. It summarizes the implications for persistence from the literatures on “stickyinformation” models, learning and so-called trend inflation models, providing some new results throughout.

In this paper we investigate the sources of the important shifts in the volatility of U.S. macroeconomic variables in the postwar period. To this end, we propose the estimation of DSGE models allowing for time variation in the volatility of the structural innovations. We apply our estimation strategy to a large-scale model of the business cycle and find that investment specific technology shocks account for most of the sharp decline in volatility of the last two decades.

The views expressed in this paper are those of the authors. No responsibility for them should be attributed to the Bank of Canada. This paper was prepared at Stanford University and was not edited by the Bank of Canada. iii

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This paper reviews Bayesian methods that have been developed in recent years to estimate and evaluate dynamic stochastic general equilibrium (DSGE) models. We consider the estimation of linearized DSGE models, the evaluation of models based on Bayesian model checking, posterior odds comparisons, and comparisons to vector autoregressions, as well as the nonlinear estimation based on a second-order accurate model solution. These methods are applied to data generated from correctly specified and misspecified linearized DSGE models, and a DSGE model that was solved with a second-order perturbation method. (JEL C11, C32, C51, C52)

Abstract. This paper presents an estimated model with learning and provides evidence that learning can improve the …t of popular monetary DSGE models and endogenously generate realistic levels of persistence. The paper starts with an agnostic view, developing a model that nests learning and some of the structural sources of persistence, such as habit formation in consumption and in‡ation indexation, that are typically needed in monetary models with rational expectations to match the persistence of macroeconomic variables. I estimate the model by likelihood-based Bayesian methods, which allow the estimation of the learning gain coe ¢ cient jointly with the ‘deep’parameters of the economy. The empirical results show that when learning replaces rational expectations, the estimated degrees of habits and indexation drop near zero. This …nding suggests that persistence arises in the model economy mainly from expectations and learning. The posterior model probabilities show that the speci…cation with learning …ts signi…cantly better than does the speci…cation with rational expectations. Finally, if learning rather than mechanical sources of persistence provides a more appropriate representation of the economy, the implied optimal policy will be di¤erent. The policymaker will

What accounts for the significant real effects of monetary policy shocks? And what accounts for the persistent and hump shaped responses of output and inflation in response to such shocks? These questions are investigated in a model that incorporates labor market search, habit persistence, sticky prices, and policy inertia. While habit persistence and price stickiness are important for the hump shaped output response and the long, drawn out inflation response, respectively, labor market frictions increase the output response and reduce the inflation response relative to an otherwise similar model based on a Walrasian labor market. Significantly, policy inertia itself is found to be the most important factor in accounting for the magnitude of the output effects of policy shocks in the model.

Please note: computational results reported in this paper are still preliminary. In particular, the number of draws from the MCMC algorithms has to be increased to raise the precision of the estimates.

In Bayesian analysis of dynamic stochastic general equilibrium (DSGE) models, prior distributions for some of the taste-and-technology parameters can be obtained from microeconometric or presample evidence, but it is difficult to elicit priors for the parameters that govern the law of motion of unobservable exogenous processes. Moreover, since it is challenging to formulate beliefs about the correlation of parameters, most researchers assume that all model parameters are independent of each other. We provide a simple method of constructing prior distributions for a subset of DSGE model parameters from beliefs about the moments of the endogenous variables. We use our approach to investigate the importance of nominal rigidities and show how the specification of prior distributions affects our assessment of the relative importance of different frictions.