Academic journal article Federal Reserve Bulletin

Summary of Papers Presented at the Conference "Models and Monetary Policy: Research in the Tradition of Dale Henderson, Richard Porter, and Peter Tinsley"

Academic journal article Federal Reserve Bulletin

Summary of Papers Presented at the Conference "Models and Monetary Policy: Research in the Tradition of Dale Henderson, Richard Porter, and Peter Tinsley"

Article excerpt

On March 26 and 27, 2004, the Federal Reserve Board held a conference in Washington, D.C., on the application of economic models to the analysis of monetary policy issues. The papers presented at the conference addressed several topics that, because they are of interest to central bankers, have been a prominent feature of Federal Reserve research over the years. In particular, the papers represent research in the tradition of work carried out over the past thirty-five years at the Federal Reserve by three prominent staff economists--Dale W. Henderson, Richard D. Porter, and Peter A. Tinsley. Thus, the conference partly served as a celebration of the contributions made by these individuals to policy-related research since the late 1960s.

Among the specific topics addressed at the conference were the influence of uncertainty on policymaking; the design of formal rules to guide policy actions; the role of money in the transmission of monetary policy; the determination of asset prices; and econometric techniques for estimating dynamic models of the economy. This summary discusses the papers in the order presented at the conference. (1)

LARS PETER HANSEN AND THOMAS J. SARGENT

One way that economists gain insights about how to make sound economic decisions in an uncertain world is to study simple problems in which the optimal way to behave can be unambiguously derived. In the 1950s, Herbert Simon and Henri Theil derived a simple principle that has been central to the study of economic decisionmaking under uncertainty. (2) Under their assumptions, they show that the optimal choice under uncertainty can be derived in two steps: First, form your best forecast of the relevant unknown variables, and second, act as you would if you were certain that your forecast would come true. This result has come to be known as the certainty-equivalence principle: Once one forms the best forecast of future conditions, the nature and the degree of uncertainty play no further role in decision-making. As might be expected, certainty equivalence applies only under very restrictive conditions, and economists have extensively studied cases in which the certainty-equivalence principle does not generate the best possible decisions. Nonetheless, certainty equivalence remains an important benchmark case to consider and has proven extremely useful both in understanding more-complicated theoretical cases and in thinking about real-world problems.

A critical assumption underlying the certainty-equivalence principle is that decisionmakers, be they households, firms, or policymakers, know the true model of the economy. No one knows, of course, the full, true nature of the economy. Thus, households, firms, and policymakers may find it appropriate to take this uncertainty into account in deciding how to act. In "'Certainty Equivalence' and 'Model Uncertainty'," Lars Peter Hansen and Thomas J. Sargent consider economic decisionmaking under model uncertainty. In their paper, the decisionmaker does not know the true model of the economy but knows only a set of models containing the true model. The authors' approach differs from Bayesian decision theory, in which the decisionmaker assigns to each model a probability that it is the true one and then chooses the decision that is the best response on average across all the competing models. Instead, Hansen and Sargent consider a form of "robust decisionmaking" in which the decisionmaker chooses the decision that maximizes his or her welfare in the worst-case scenario--that is, when the true model turns out to be the worst possible model from the standpoint of the agent. Robust decisionmaking is quite complicated, especially if what happens to be the worst-case model depends on which decision the agent chooses.

The paper shows that, even under this cautious approach to taking account of model uncertainty, a surprising and useful version of the certainty-equivalence principle prevails. …

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