Desirability of Nominal GDP Targeting Under Adaptive Learning

Nominal GDP targeting has been advocated by a number of authors since it produces relative stability of inflation and output. However, all of the papers assume rational expectations on the part of private agents. In this paper I provide an analysis of this assumption. I use stability under recursive learning as a criterion for evaluating nominal GDP targeting in the context of a model with explicit micro- foundations which is currently the workhorse for the analysis of monetary policy. The results of the paper provide support for such a monetary policy.Nominal GDP; learning; expectational stability.

Is more data better?

Conventional wisdom usually suggests that agents should use all the data they have to make the best possible prediction. In this paper, however, it is shown that agents may sometimes be able to make better predictions by throwing away old data. The optimality criterion agents adopt is the mean squared error criterion.mean squared error; prediction; optimality.

Aggregating Infinite Utility Streams with Inter-generational Equity: The Impossibility of Being Paretian

It has been known that, in aggregating infinite utility streams, there does not exist any social welfare function, which satisfies the axioms of Pareto, inter-generational equity and continuity. We show that the impossibility result persists even without imposing the continuity axiom, and in frameworks allowing for more general domains of utilities than those used in the existing literature.

Determinacy, learnability, and monetary policy inertia

We document that monetary policy inertia can help alleviate problems of indeterminacy and non-existence of stationary equilibrium observed for some commonly-studied monetary policy rules. We also find that inertia promotes learnability of equilibrium. The context is a simple, forward-looking model of the macroeconomy widely used in the rapidly expanding literature in this area. We conclude that this might be an important reason why central banks in the industrialized economies display considerable inertia when adjusting monetary policy in response to changing economic conditions.Monetary policy ; Monetary theory

Learning with Bounded Memory in Stochastic Models

Learning with bounded memory in stochastic frameworks is incomplete in the sense that the learning dynamics cannot converge to an rational expectations equilibrium (REE). The properties of the dynamics arising from such rules are studied for models with steady states. If in standard linear models the REE is in a certain sense expectationally stable (E-stable), then the dynamics are asymptotically stationary and forecasts are unbiased. We also provide similar local results for a class of nonlinear models with small noise and their approximations.Bounded memory; expectational stability; unbiased.

Determinacy, Learnability, and Monetary Policy Inertia

We evaluate Taylor-type monetary policy rules from the perspective of which classes of rules most reliably induce determinacy and learnability of a rational expectations equilibrium. The context is a simple, forward-looking model of the macroeconomy widely used in the rapidly expanding literature in this area. The policy rules we consider have an inertial component, whereby the central bank can respond cautiously to economic events. We document that policy inertia can help alleviate problems of indeterminacy and explosive instability of equilibrium in this model, and that learnability of equilibrium is not impaired by policymaker caution. We conclude that this might be an important reason why central banks in the industrialized economies display considerable inertia when adjusting monetary policy in response to changing economic conditions.Monetary policy rules; determinacy; learnability; instrument instability.

Adaptive Learning in Stochastic Nonlinear Models When Shocks Follow a Markov Chain.

Local convergence results for adaptive learning of stochastic steady states in nonlinear models are extended to the case where the exogenous observable variables follow a ?nite Markov chain. The stability conditions for the corresponding nonstochastic model and its steady states yield convergence for the stochastic model when shocks are suf?ciently small. The results are applied to asset pricing and to an overlapping generations model. Large shocks can destabilize learning even if the steady state is stable with small shocks.bounded rationality; recursive algorithms; steady state; linearization; asset pricing; overlapping generations

Learning Stability in Economies with Heterogenous Agents

An economy exhibits structural heterogeneity when the forecasts of different agents have different effects on the determination of aggregate variables. Various forms of structural heterogeneity can arise and we study the important case of economies in which agents' behavior depends on forecasts of aggregate variables and show how different forms of heterogeneity in structure, forecasts, and adaptive learning rules affect the conditions for convergence of adaptive learning towards rational expectations equilibrium. Results are applied to the market model with supply lags and a New Keynesian model of interest rate setting.adaptive learning, expectations formation, stability of equilibrium, market model, monetary policy.