Rational expectations provides an elegant and powerful framework that has come to dominate thinking about the dynamic structure of the economy and econometric policy evaluation over the past 30 years. This success has spurred further examination into the strong information assumptions implicit in many of its applications. Thomas Sargent (1993) concludes that “rational expectations models impute much more knowledge to the agents within the model ... than is possessed by an econometrician, who faces estimation and inference problems that the agents in the model have somehow solved” (p. 3, emphasis in original).1 Researchers have proposed refinements to rational expectations that respect the principle that agents use information efficiently in forming expectations, but nonetheless recognize the limits to and costs of information-processing and cognitive constraints that influence the expectations-formation process (Sargent 1999, Evans and Honkapohja 2001, Sims 2003).
In this study, we allow for a form of imperfect knowledge in which economic agents rely on an adaptive learning technology to form expectations. This form of learning represents a relatively modest deviation from rational expectations that nests it as a limiting case. We show that the resulting process of perpetual learning introduces an additional layer of interaction between monetary policy and economic outcomes that has important implications for macroeconomic dynamics and for monetary policy design. As we illustrate, monetary policies that would be efficient under rational expectations can perform poorly when knowledge is imperfect. In particular, with imperfect knowledge, policies that fail to maintain tight control over inflation are prone to episodes in which the public’s expectations of inflation become uncoupled from the policy objective. The presence of this imperfection makes stabilization policy more difficult than would appear under rational expectations and highlights the value of effectively communicating a central bank’s inflation objective and of continued vigilance against inflation in anchoring inflation expectations and fostering macroeconomic stability.
In this paper, we investigate the macroeconomic implications of a process of “perpetual learning.” Our work builds on the extensive literature relating rational expectations with learning and the adaptive formation of expectations (Bray 1982, Bray and Savin 1984, Marcet and Sargent 1989, Woodford, 1990, Bullard and Mitra 2002). A key finding in this literature is that under certain conditions an economy with learning converges to the rational expectations equilibrium (Townsend 1978, Bray 1982, 1983, Blume and Easley 1982). However, until agents have accumulated sufficient knowledge about the economy, economic outcomes during the transition depend on the adaptive learning process (Lucas 1986). Moreover, in a changing economic environment, agents are constantly learning and their beliefs converge not to a fixed rational expectations equilibrium, but to an ergodic distribution around it (Sargent 1999, Evans and Honkapohja 2001).
As a laboratory for our experiment, we employ a simple linear model of the U.S. economy with characteristics similar to more elaborate models frequently used to study optimal monetary policy. We assume that economic agents know the correct structure of the economy and form expectations accordingly. But, rather than endowing them with complete knowledge of the parameters of these functions—as would be required by imposing the rational expectations assumption—we posit that economic agents rely on finite memory least squares estimation to update these parameter estimates. This setting conveniently nests rational expectations as the limiting case corresponding to infinite memory least squares estimation and allows varying degrees of imperfection in expectations formation to be characterized by variation in a single model parameter.
Download
Imperfect Knowledge, Inflation Expectations, and Monetary Policy
