Collaborative Research: Monetary Policy in a Data-Rich Environment

Princeton University, Princeton NJ

Investigators

Abstract

The collaborative and individual empirical research planned in this project will analyze the determinants and effects of monetary policy, taking explicitly into account the fact that policy is made in a data-rich environment in which monetary policymakers can exploit information contained in hundreds or even thousands of macroeconomic data series. Our study contrasts with most previous work, which typically assumes that the Federal Reserve's information set is spanned by ten variables or less. We will also take into account that monetary policy is made in real time; that is, instead of assuming (contrary to fact) that the Fed has final revisions of data at hand, we use only data that were actually available to the Fed at each date. To do this we use a real-time data set constructed by others to which we add non-revised data from various Federal Reserve documents and other federal agencies. Our basic task is to estimate policy reaction functions (PRFs) for the Federal Reserve, which relate the Fed's instrument to measures of the state of the economy. As far as possible, we want to allow the state of the economy to depend on the full range of macroeconomic data available at the time. To make estimation feasible we need a dimension-reduction scheme. We will apply a dynamic factor model approach, to be estimated using methods devised by others in a forecasting context. Besides allowing us to work with data sets in which the number of series exceeds the number of observations, this method permits us to deal systematically with data irregularities, including data of different frequencies and different spans. As already indicated, we are also able to incorporate the fact that at each date the Fed may be looking at a different vintage (revision) of the same underlying data series. We will allow for time variation in the parameters of the PRF, using a method that makes estimation of such models computationally easy in this context. Finally, the Fed's PRF can be estimated jointly with equations describing the rest of the economy, or in an unrestricted manner that leaves the rest of the economy unspecified. We are interested in addressing several questions with the empirical analyses. On the positive side we want to see if information-intensive PRF's provide a better description of the historical conduct of monetary policy than other alternatives. We also want to determine what types of information the Fed has historically responded to and to see if the information that appears to affect Fed decisions can be explained in terms of its predictive content for inflation and real activity. If that latter appear not to matter, then we will explore what can account for the Fed's behavior. If we are able to obtain sharper estimates of both the systematic and non-systematic components of monetary policy, we hope to use these results to refine recent work on the effects of each of these components on the economy. From a normative point of view, we would like to address the question of just how monetary policy should be conducted in a data-rich environment. This is a more complex question than the problem of forecasting with many variables, since the Fed's decision problem is dynamic and its loss function may have very different properties from a standard econometric loss function. We believe that addressing such questions will be of practical value, as well as providing a more realistic description of what the Fed actually does.