Recent macroeconomic research has devoted considerable efforts to the development and estimation of dynamic stochastic equilibrium (DSGE) models that are internally consistent, and based on first principles. Some recent micro-founded DSGE models, which involve numerous frictions and various types of shocks, appear to replicate the data in important dimensions and have appreciable out-of-sample forecasting performance. In part motivated by these promising results, these models are now increasingly perceived as useful forecasting devices and as valuable inputs to policy making. However, one potentially important limitation of these empirical models is that they exploit only a handful of macroeconomic indicators. This is at odds with the fact that central banks and market participants monitor hundreds of economic indicators and is inconsistent with state-of-the-art forecasting models that emphasize the importance of large sets of macroeconomic indicators.
The first step of this project develops a general empirical framework for the estimation of DSGE models which makes efficient use of the large amount of available data. Instead of assuming that theoretical concepts (e.g., inflation and output) are properly measured by a single data series, these are treated as imperfectly observed through many noisy indicators. Information from indicators that have an unknown relationship with the model's concepts can also be exploited. The estimation is based on a new variant of an MCMC algorithm, which deals effectively with the high dimensionality of the estimation problem, and can inherit the properties of classical maximum likelihood estimation. The proposed empirical framework has several advantages: 1) it helps to better identify structural shocks from measurement errors; 2) it potentially makes the estimation more efficient which could improve the model's forecasting performance; 3) it yields latent variables that have a clear economic interpretation, unlike in macroeconomic applications of dynamic factor models; 4) it has predictions for all series included in the data set; 5) it provides a natural way to document the sensitivity of the results to the priors.
The second step applies this empirical framework to a state-of-the-art DSGE model, a variant of the Smets and Wouters (2004) model, which has shown promising empirical successes. Preliminary results show that adding more information affects important conclusions about business cycles and improves significantly the model's forecasting performance.
The third step derives a data-rich robustly optimal target criterion for monetary policy. Existing derivations are silent about how to optimally exploit information from large data sets, which might be a crucial operational consideration. This part of the project combines, within the data-rich empirical framework, existing procedures for the derivation of a robustly optimal target criterion, with existing results for optimal monetary policy under imperfect information.
Broader Impacts: The objective of this proposal is to contribute to the development of an operational model that can produce useful, real-time, policy prescriptions. The proposed approach could help central banks systematically process a large amount of information in real-time, through the lens of a structural model that maintains an interpretable link between various economic developments, forecasts and policy prescriptions. It could also be useful to the private sector, by providing a benchmark of the optimal policy stance and implied forecasts for a wide range of economic indicators. A concrete output of this project is to develop an infrastructure allowing the PIs to continuously estimate, forecast, and calculate the optimal policy setting in real time, which will be published on a website (together with the data and various computer routines underlying the estimation). Finally, the operational convenience of the proposed framework makes it particularly appealing as a teaching tool. The investigators plan to expand an existing MBA case on the basis of the results from this proposal.
