This paper investigates the predictions of a simple optimizing model of nominal price rigidity for the aggregate price level and the dynamics of inflation. I compare the model’s predictions with those of a perfectly competitive, flexible price ‘benchmark’ model (corresponding to the model of pricing assumed in standard real business cycle models), and evaluate how much the introduction of nominal rigidities improves the model’s fit with the data. The model’s predictions are derived using only the firms optimal pricing problem; taking as given the paths of nominal labor compensation, labor productivity, and output, I determine the implied path of prices predicted by the model. Because prices are not a stationary series, I present my results in terms of the predicted path of the price/unit labor cost ratio, where the parameters characterizing such paths are chosen to maximize the fit with the data. I find that, while the evolution of prices relative to unit labor costs is quite different from what would be predicted by the flexible-price ‘benchmark ’ model, a simple model of nominal price rigidity delivers an extremely close approximation both of the price/unit labor cost ratio and of the inflation series, even under a very simple approach to the measurement of marginal costs. Moreover, the results are robust to modifications of this measure.

JEL No. C10 The major contributions of twentieth century econometrics to knowledge were the definition of causal parameters when agents are constrained by resources and markets and causes are interrelated, the analysis of what is required to recover causal parameters from data (the identification problem), and clarification of the role of causal parameters in policy evaluation and in forecasting the effects of policies never previously experienced. This paper summarizes the development of those ideas by the Cowles Commission, the response to their work by structural econometricians and VAR econometricians, and the response to structural and VAR econometrics by calibrators, advocates of natural and social experiments, and by nonparametric econometricians and statisticians.

We study how decision makers' concerns about robustness affect prices and quantities in a stochastic growth model. In the model economy, growth rates in technology are altered by infrequent large shocks and continuous small shocks. An investor observes movements in the technology level but cannot perfectly distinguish their sources. Instead the investor solves a signal extraction problem. We depart from most of the macroeconomics and finance literature by presuming that the investor treats the specification of technology evolution as an approximation. To promote a decision rule that is robust to model misspecification, an investor acts as if a malevolent player threatens to perturb the actual data generating process relative to his approximating model. We study how a concern about robustness alters asset prices. We show that the dynamic evolution of the risk-return tradeoff is dominated by movements in the growth-state probabilities and that the evolution of the dividend-price ratio is driven primarily by the capital-technology ratio.

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)

Numerical software is central to our computerized society; it is used... to analyze future options for financial markets and the economy. It is essential that it be of high

The mechanisms governing the relationship of money, prices and interest rates to the business cycle are one of the most studied and most disputed topics in macroeconomics. In this paper, we first document key empirical aspects of this relationship. We then ask how well three benchmark rational expectations macroeconomic models --- a real business cycle model, a sticky price model and a liquidity effect model --- account for these central facts. While the models have diverse successes and failures, none can account for the fact that both real and nominal interest rates are "inverted leading indicators" of real economic activity. That is, none of the models captures the post-...

Academic thinking about monetary economics—as well as macroeconomics more generally—has altered drastically since 1971–1973 and so has the practice of monetary policy. The former has passed through the rational expectations and real-business-cycle revolutions into today’s “new neoclassical synthesis”

This paper employs the one-sector Real Business Cycle model as a testing ground for four di®erent procedures to estimate Dynamic Stochastic General Equilibrium (DSGE) models. The procedures are: 1) Maximum Likelihood (with and without measurement errors and incorporating priors), 2) Generalized Method of Moments, 3) Simulated Method of Moments, and 4) the Extended Method of Simulated Moments proposed by Smith (1993). Monte Carlo analysis shows that although all procedures deliver reasonably good estimates, there are substantial di®erences in statistical and computational e±ciency in the small samples currently available to estimate DSGE models. The implications of the singularity of DSGE models for each estimation procedure are fully discussed.

This paper presents a method to perform likelihood-based inference in nonlinear dynamic equilibrium economies. This type of models has become a standard tool in quantitative economics. However, existing literature has been forced so far to use moment procedures or linearization techniques to estimate these models. This situation is unsatisfactory: moment procedures suffer from strong small samples biases and linearization depends crucially on the shape of the true policy functions, possibly leading to erroneous answers. We propose the use of Sequential Monte Carlo methods to evaluate the likelihood function implied by the model. Then we can perform likelihood-based inference, either searching for a maximum (Quasi-Maximum Likelihood Estimation) or simulating the posterior using a Markov Chain Monte Carlo algorithm (Bayesian Estimation). We can also compare different models even if they are nonnested and misspecified. To perform classical model selection, we follow Vuong (1989) and use the Kullback-Leibler dis- tance to build Likelihood Ratio Tests. To perform Bayesian model comparison, we build Bayes factors. As an application, we estimate the stochastic neoclassical growth model.

This paper grew out of the Panel Discussion of the workshop ”SDGE Models and their use in monetary policy ” , held at the European Central Bank, June 5-6, 2001. I would like to thank the participants of the TSM conference in Touluse for comments and suggestions. 1 1