Commentary on The challenges of estimating potential output in real time

Economic development ; Economic conditions

Real-time model uncertainty in the United States: the Fed from 1996-2003

We study 30 vintages of FRB/US, the principal macro model used by the Federal Reserve Board staff for forecasting and policy analysis. To do this, we exploit archives of the model code, coefficients, baseline databases and stochastic shock sets stored after each FOMC meeting from the model’s inception in July 1996 until November 2003. The period of study was one of important changes in the U.S. economy with a productivity boom, a stock market boom and bust, a recession, the Asia crisis, the Russian debt default, and an abrupt change in fiscal policy. We document the surprisingly large and consequential changes in model properties that occurred during this period and compute optimal Taylor-type rules for each vintage. We compare these optimal rules against plausible alternatives. Model uncertainty is shown to be a substantial problem; the efficacy of purportedly optimal policy rules should not be taken on faith. JEL Classification: E37, E5, C5, C6monetary policy, real-time analysis, uncertainty

Optimal Policy Projections

We outline a method to provide advice on optimal monetary policy while taking policymakers' judgment into account. The method constructs Optimal Policy Projections (OPPs) by extracting the judgment terms that allow a model, such as the Federal Reserve Board's FRB/US model, to reproduce a forecast, such as the Greenbook forecast. Given an intertemporal loss function that represents monetary policy objectives, OPPs are the projections - of target variables, instruments, and other variables of interest -that minimize that loss function for given judgment terms. The method is illustrated by revisiting the Greenbook forecasts of February 1997 and November 1999, in each case using the vintage of the FRB/US model that was in place at that time. These two particular forecasts were chosen, in part, because they were at the beginning and the peak, respectively, of the late 1990s boom period. As such, they differ markedly in their implied judgments of the state of the world, and our OPPs illustrate this difference. For a conventional loss function, our OPPs provide significantly better performance than Taylor-rule simulations.

Optimal Policy Projections

We outline a method to provide advice on optimal monetary policy while taking policymakers’ judgment into account. The method constructs optimal policy projections (OPPs) by extracting the judgment terms that allow a model, such as the Federal Reserve Board staff economic model, FRB/US, to reproduce a forecast, such as the Greenbook forecast. Given an intertemporal loss function that represents monetary policy objectives, OPPs are the projections — of target variables, instruments, and other variables of interest — that minimize that loss function for given judgment terms. The method is illustrated by revisiting the economy of early 1997 as seen in the Greenbook forecasts of February 1997 and November 1999. In both cases, we use the vintage of the FRB/US model that was in place at that time. These two particular forecasts were chosen, in part, because they were at the beginning and the peak, respectively, of the late 1990s boom period. As such, they differ markedly in their implied judgments of the state of the world in 1997 and our OPPs illustrate this difference. For a conventional loss function, our OPPs provide significantly better performance than Taylor-rule simulations.

Robustifying Learnability

In recent years, the learnability of rational expectations equilibria (REE) and determinacy of economic structures have rightfully joined the usual performance criteria among the sought after goals of policy design. And while some contributions to the literature (for example Bullard and Mitra (2001) and Evans and Honkapohja (2002)) have made significant headway in establishing certain features of monetary policy rules that facilitate learning, a comprehensive treatment of policy design for learnability has yet to surface, especially for cases in which agents have potentially misspecified their learning models. This paper provides such a treatment. We argue that since even among professional economists a generally acceptable workhorse model of the economy has not been agreed upon, it is unreasonable to expect private agents to have collective rational expectations. We assume instead that agents have an approximate understanding of the workings of the economy and that their task of learning true reduced forms of the economy is subject to potentially destabilizing errors. We then ask: can a central bank set policy that accounts for learning errors but also succeeds in bounding them in a way that allows eventual learnability of the model, given policy. For different parameterizations of a given policy rule applied to a New Keynesian model, we use structured singular value analysis (from robust control) to find the largest ranges of misspecifications that can be tolerated in a learning model without compromising convergence to an REE. A parallel set of experiments seeks to determine the optimal stance (strong inflation as opposed to strong output stabilization) that allows for the greatest scope of errors in learning without leading to expectational instabilty in cases when the central bank designs both optimal and robust policy rules with commitment. We compare the features of all the rules contemplated in the paper with those that maximize economic performance in the true model, and we measure the performance cost of maximizing learnability under the various conditions mentioned here.monetary policy, learning, E-stability, model uncertainty, robustness

Robustifying learnability

In recent years, the learnability of rational expectations equilibria (REE) and determinacy of economic structures have rightfully joined the usual performance criteria among the sought-after goals of policy design. Some contributions to the literature, including Bullard and Mitra (2001) and Evans and Honkapohja (2002), have made significant headway in establishing certain features of monetary policy rules that facilitate learning. However a treatment of policy design for learnability in worlds where agents have potentially misspecified their learning models has yet to surface. This paper provides such a treatment. We begin with the notion that because the profession has yet to settle on a consensus model of the economy, it is unreasonable to expect private agents to have collective rational expectations. We assume that agents have only an approximate understanding of the workings of the economy and that their learning the reduced forms of the economy is subject to potentially destabilizing perturbations. The issue is then whether a central bank can design policy to account for perturbations and still assure the learnability of the model. Our test case is the standard New Keynesian business cycle model. For different parameterizations of a given policy rule, we use structured singular value analysis (from robust control theory) to find the largest ranges of misspecifications that can be tolerated in a learning model without compromising convergence to an REE.Robust control ; Monetary policy

Robustifying learnability

In recent years, the learnability of rational expectations equilibria (REE) and determinacy of economic structures have rightfully joined the usual performance criteria among the sought-after goals of policy design. Some contributions to the literature, including Bullard and Mitra (2001) and Evans and Honkapohja (2002), have made significant headway in establishing certain features of monetary policy rules that facilitate learning. However a treatment of policy design for learnability in worlds where agents have potentially misspecified their learning models has yet to surface. This paper provides such a treatment. We begin with the notion that because the profession has yet to settle on a consensus model of the economy, it is unreasonable to expect private agents to have collective rational expectations. We assume that agents have only an approximate understanding of the workings of the economy and that their learning the reduced forms of the economy is subject to potentially destabilizing perturbations. The issue is then whether a central bank can design policy to account for perturbations and still assure the learnability of the model. Our test case is the standard New Keynesian business cycle model. For different parameterizations of a given policy rule, we use structured singular value analysis (from robust control theory) to find the largest ranges of misspecifications that can be tolerated in a learning model without compromising convergence to an REE. In addition, we study the cost, in terms of performance in the steady state of a central bank that acts to robustify learnability on the transition path to REE. (Note: This paper contains full-color graphics) JEL Classification: C6, E5E-stability, learnability, Learning, monetary policy, robust control

Optimal Policy Projections

We outline a method to provide advice on optimal monetary policy while taking policymakers’ judgment into account. The method constructs optimal policy projections (OPPs) by extracting the judgment terms that allow a model, such as the Federal Reserve Board staff economic model, FRB/US, to reproduce a forecast, such as the Greenbook forecast. Given an intertemporal loss function that represents monetary policy objectives, OPPs are the projections — of target variables, instruments, and other variables of interest — that minimize that loss function for given judgment terms. The method is illustrated by revisiting the economy of early 1997 as seen in the Greenbook forecasts of February 1997 and November 1999. In both cases, we use the vintage of the FRB/US model that was in place at that time. These two particular forecasts were chosen, in part, because they were at the beginning and the peak, respectively, of the late 1990s boom period. As such, they differ markedly in their implied judgments of the state of the world in 1997 and our OPPs illustrate this difference. For a conventional loss function, our OPPs provide significantly better performance than Taylor-rule simulations