Assessing changes in the monetary transmission mechanism: a VAR approach

Paper for a conference sponsored by the Federal Reserve Bank of New York entitled Financial Innovation and Monetary TransmissionEconomic conditions - United States ; Monetary policy

DSGE Models in a Data-Rich Environment

Standard practice for the estimation of dynamic stochastic general equilibrium (DSGE) models maintains the assumption that economic variables are properly measured by a single indicator, and that all relevant information for the estimation is summarized by a small number of data series. However, recent empirical research on factor models has shown that information contained in large data sets is relevant for the evolution of important macroeconomic series. This suggests that conventional model estimates and inference based on estimated DSGE models might be distorted. In this paper, we propose an empirical framework for the estimation of DSGE models that exploits the relevant information from a data-rich environment. This framework provides an interpretation of all information contained in a large data set, and in particular of the latent factors, through the lenses of a DSGE model. The estimation involves Markov-Chain Monte-Carlo (MCMC) methods. We apply this estimation approach to a state-of-the-art DSGE monetary model. We find evidence of imperfect measurement of the model's theoretical concepts, in particular for inflation. We show that exploiting more information is important for accurate estimation of the model's concepts and shocks, and that it implies different conclusions about key structural parameters and the sources of economic fluctuations.

DSGE Models in a Data-Rich Environment

Standard practice for the estimation of dynamic stochastic general equilibrium (DSGE) models maintains the assumption that economic variables are properly measured by a single indicator, and that all relevant information for the estimation is summarized by a small number of data series. However, recent empirical research on factor models has shown that information contained in large data sets is relevant for the evolution of important macroeconomic series. This suggests that conventional model estimates and inference based on estimated DSGE models might be distorted. In this paper, we propose an empirical framework for the estimation of DSGE models that exploits the relevant information from a data-rich environment. This framework provides an interpretation of all information contained in a large data set, and in particular of the latent factors, through the lenses of a DSGE model. The estimation involves Markov-Chain Monte-Carlo (MCMC) methods. We apply this estimation approach to a state-of-the-art DSGE monetary model. We find evidence of imperfect measurement of the model's theoretical concepts, in particular for inflation. We show that exploiting more information is important for accurate estimation of the model's concepts and shocks, and that it implies different conclusions about key structural parameters and the sources of economic fluctuations.

Optimal Inflation Targeting Rules

This paper characterizes optimal monetary policy for a range of alternative economic models in terms of a flexible inflation targeting rule, with a target criterion that depends on the model specification. It shows which forecast horizons should matter, and which variables besides inflation should be taken into account, for each specification. The likely quantitative significance of the various factors considered in the general discussion is then assessed by estimating a small, structural model of the U.S. monetary transmission mechanism with explicit optimizing foundations. An optimal policy rule is computed for the estimated model, and shown to correspond to a multi-stage inflation-forecast targeting procedure. The degree to which actual U.S. policy over the past two decades has conformed to the optimal target criteria is then considered.

Has Monetary Policy Become More Effective?

Recent research provides evidence of important changes in the U.S. economic environment over the last 40 years. This appears to be associated with an alteration of the monetary transmission mechanism. In this paper we investigate the implications for the evolution of monetary policy effectiveness. Using an identified VAR over the pre- and post-1980 periods we first provide evidence of a reduction in the effect of monetary policy shocks in the latter period. We then present and estimate a fully specified model that replicates well the dynamic response of output, inflation, and the federal funds rate to monetary policy shocks in both periods. Using the estimated structural model, we perform counterfactual experiments to determine the source of the observed change in the monetary transmission mechanism, as well as in the economy's response to supply and demand shocks. The main finding is that monetary policy has been more stabilizing in the recent past, as a result of both the way it has responded to shocks, but also by ruling out non-fundamental fluctuations.

Optimal Interest-Rate Rules: II. Applications

In this paper we calculate robustly optimal monetary policy rules for several variants of a simple optimizing model of the monetary transmission mechanism with sticky prices and/or wages. We discuss representations of optimal policy both in terms of interest-rate feedback rules that generalize the well-known Taylor rule,' and in terms of commitment to a target criterion of the kind discussed in familiar proposals for flexible inflation targeting.' Optimal rules, however, require that policy be history-dependent in ways not contemplated by many well-known proposals. We furthermore find that a robustly optimal policy rule is almost inevitably an implicit rule, that requires the central bank to use a structural model to project the economy's evolution under the contemplated policy action. Finally, our numerical examples suggest that optimal rules do not place nearly as much weight on projections of inflation or output many quarters in the future as occurs under the current practice of inflation-forecast targeting central banks.

Sticky prices and monetary policy : evidence from disaggregated U.S. data

This paper uses factor-augmented vector autoregressions (FAVAR) estimated using a large data set to disentangle fluctuations in disaggregated consumer and producer prices which are due to macroeconomic factors from those due to sectorial conditions. This allows us to provide consistent estimates of the effects of US monetary policy on disaggregated prices. While sectorial prices respond quickly to sector-specific shocks, we find that for a large number of price series, there is a significant delay in the response of prices to monetary policy shocks. In addition, price responses display little evidence of a “price puzzle,” contrary to existing studies based on traditional VARs. The observed dispersion in the reaction of producer prices is relatively well explained by the degree of market power, as predicted by models with monopolistic competition. JEL Classification: E32, E5

Sticky Prices and Monetary Policy: Evidence from Disaggregated U.S. Data

This paper disentangles fluctuations in disaggregated prices due to macroeconomic and sectoral conditions using a factor-augmented vector autoregression estimated on a large data set. On the basis of this estimation, we establish eight facts: (1) Macroeconomic shocks explain only about 15% of sectoral inflation fluctuations; (2) The persistence of sectoral inflation is driven by macroeconomic factors; (3) While disaggregated prices respond quickly to sector-specific shocks, their responses to aggregate shocks are small on impact and larger thereafter; (4) Most prices respond with a significant delay to identified monetary policy shocks, and show little evidence of a "price puzzle," contrary to existing studies based on traditional VARs; (5) Categories in which consumer prices fall the most following a monetary policy shock tend to be those in which quantities consumed fall the least; (6) The observed dispersion in the reaction of producer prices is relatively well explained by the degree of market power; (7) Prices in sectors with volatile idiosyncratic shocks react rapidly to aggregate monetary policy shocks; (8) The sector-specific components of prices and quantities move in opposite directions.