An Estimated Dynamic, General Equilibrium Model for Monetary Policy Analysis

We describe a strategy for fitting a multiple shock, dynamic general equilibrium model to the data. In this draft, we describe results for a version of the model with monetary policy shocks and with persistent shocks to technology. The model does well at reproducing the dynamic effects of these shocks. ∗Christiano and Eichenbaum are grateful for the financial support of a National Science Foundation grant to the National Bureau of Economic Research. We are grateful for helpful comments from Susanto Basu, Valerie Ramey and Harald Uhlig. We particularly want to thank Eduard Pelz for excellent research assistance. This paper does not necessarily reflect the views of the Federal Reserve Bank of Chicago, the Federal Reserve Bank of Cleveland, or the Riksbank. †Federal Reserve Bank of Cleveland ‡Northwestern University, National Bureau of Economic Research, and Federal Reserve Banks of Chicago and Cleveland. §Northwestern University, National Bureau of Economic Research, and Federal Reserve Bank of Chicago. ¶Riksbank Our objective is to construct a dynamic general equilibrium model that matches the macroeconomic time series in detail. We want a model that can be used to address classic questions: what are the shocks that drive the business cycle? what is the optimal response of monetary policy to these shocks? is there a simple policy rule like a Taylor rule which comes close to supporting, as the unique equilibrium, the best outcome? is there a time consistency problem associated with this policy? To develop credible answers questions like these requires a model that reproduces most of the variation in the data. The model we work with is a multishock version of the model estimated in Christiano, Eichenbaum and Evans (2002) (CEE). Our estimation strategy focuses on matching dynamic shock responses in the data to their counterparts in the model. The CEE model seems like a natural starting point, because the model has considerable success in matching impulse responses to a monetary policy shock. Our model incorporates two types of disturbances: financial market shocks and nonfinancial market shocks. The former include a shock to household money demand, a shock to firm money demand, and a monetary policy shock. Non-financial market shocks include a persistent and a transitory shock to the technology for producing final goods, a shock to the technology for producing investment goods, a shock to the market power of firms and a shock to the market power of household labor suppliers. It has been argued that, in one way or another, each of these disturbances is important for the analysis of monetary policy questions. There is a large literature that stresses the importance of shocks to technology as impulses to the business cycle. There are also several papers which emphasize the role in business cycles of disturbances to household market power, which in our context are isomorphic to disturbances to preferences for leisure. Money demand shocks have been deemed to be so substantial, that many analysists of monetary policy find it convenient to abstract from money altogether. Finally, the importance of monetary policy shocks lies in the clues they leave regarding the structure of the economy. To identify the effects of a monetary policy shock in the data, we adopt a recursiveness approach that has become standard in the literature. For the persistent technology shock, we follow Gali (1999) and Francis and Ramey (2001) in assuming that innovations to this shock are the only disturbance that affects the level of labor productivity in the long run. Of course, it is easy to challenge these identifying assumptions on a priori grounds. But, this is always the case with identifying restrictions. In the end, the only convincing way to build confidence in such assumptions is to see if they help us to understand the data. One of the purposes of this project is to find out. We propose a very different identification strategy for the other 6 shocks in the model. For example, the presence of endogeneity in the evolution of technology in principle has the consequence that all shocks affect the level of productivity in the long run.