- Monetary Policy Models
I have written several Brookings Papers looking at the relation of multiple-equation economic models to the process of monetary policymaking.1 When the first of these papers was written, the impact of the rational expectations critique in undermining academic interest in quantitative modeling for monetary policy was apparent. Many, maybe most, economists took the Lucas critique to imply that the month-to-month business of choosing monetary policy actions in the light of current information was trivial or irrelevant. Economists were thought to have nothing useful to say about it. They were supposed to contemplate only the choice of policy "rules," which were modeled as functions mapping the state of the economy into policy actions.
The main point of that paper was that the regularly recurring task of choosing policy actions was neither easy nor unimportant, and indeed that there is no other form of policy choice—"rules," if they can be changed, are themselves policy actions. The paper suggested methods for using a reliable probability model to evaluate conditional projections and applied these methods to a vector autoregression (VAR) model to determine that the then-current policy projections by the Council of Economic Advisers made no sense. But it provided little constructive criticism of the models then in use for policy projections. [End Page 75]
Central bank modelers by then had the idea that, to get academic respect, they should build rational expectations into their models. However, the VAR modeling style displayed in my 1982 paper provided few hooks on which to hang rational expectations. Some central banks and regional Federal Reserve banks estimated VAR forecasting models, but nowhere did such models become the central modeling tool for policy discussions.
Fourteen years later I co-wrote with Eric Leeper and Tao Zha another Brookings Paper on monetary policy models.2 By then a substantial literature modeling monetary policy and its effects with structural VARs (SVARs) had arisen. The robust findings of that literature were that
— Monetary policy in most countries and periods is best modeled as an interest rate-setting rule.
— Most variation in monetary policy instruments consists of systematic reaction to the state of the economy. Random disturbances to monetary policy existed, but they explained little of the observed business cycle variation.
— Output responds with a lag, and prices with an even longer lag, to monetary policy actions. The shapes of these estimated responses conditioned policy discussion and were used as calibration targets by non-VAR modelers.
Our paper surveyed the SVAR literature and suggested by example how SVARs could be expanded to a scale closer to that of central bank policy models. The paper still provided no hooks on which to hang Lucas critique repellent, however, and it made no connection to what was actually going on in central bank modeling, which was not SVAR based. SVARs and VARs were used as auxiliary tools in many central banks, but nowhere had they become the central focus for policy discussion.
In 2002, I visited four central banks and interviewed research staff as well as a few policy board members. The banks' models, which were in regular use as part of the month-by-month routine of preparing forecasts and "scenario" analysis, were incorporating Lucas critique repellent, but at the expense of abandoning any claim to being probability models of the data. In the Brookings Paper that came out of those interviews,3 I criticized this new generation of central bank models, but I also criticized the academic [End Page 76] econometric and macroeconomic literature, which took no interest in policy modeling and had little guidance to offer policy modelers. The paper argued that getting back to credible probability models was feasible and important, and indeed the only way to allow clear discussion of uncertain contingencies in a way relevant to decisionmaking. At the end of the paper I pointed to some promising developments, including a paper by economists at the European Central Bank that demonstrated the feasibility of constructing a dynamic stochastic general equilibrium (DSGE) model for monetary policy and producing a distribution over the uncertain values of the model parameters (a uniquely Bayesian notion).4
It is perhaps...