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.

Are Non-Fundamental Equilibria Learnable in Models of Monetary Policy?.

Recent models of monetary policy can have indeterminacy of equilibria. The indeterminacy property is often viewed as a difficulty of these models. We consider its significance using the learning approach to expectations formation by employing expectational stability as a robustness criterion for different equilibria. We derive the expectational stability and instability conditions for forward-looking multivariate models, both with and without lags, that cover a wide range of monetary policies proposed in the literature.Adaptive learning, stability, sunspots, monetary policy.

Performance of Inflation Targeting Based on Constant Interest Rate Projections

Monetary policy is sometimes formulated in terms of a target level of inflation, a fixed time horizon and a constant interest rate that is anticipated to achieve the target at the specified horizon. These requirements lead to constant interest rate (CIR) instrument rules. Using the standard New Keynesian model, it is shown that some forms of CIR policy lead to both indeterminacy of equilibria and instability under adaptive learning. However, some other forms of CIR policy perform better. We also examine the properties of the different policy rules in the presence of inertial demand and price behaviour.Indeterminacy; instability under learning; inflation targeting; inertia in demand; inflation inertia.

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.

Learning About Monetary Policy Rules

We study macroeconomic systems with forward-looking private sector agents and a monetary authority that is trying to control the economy through the use of a linear policy feedback rule. A typical finding in the burgeoning literature in this area is that policymakers should be relatively aggressive in responding to available information about the macroeconomy. A natural question to ask about this result is whether policy responses which are too aggressive might actually destabilize the economy. We use stability under recursive learning (a la Evans and Honkapohja (2000)) as a criterion for evaluating monetary policy rules in this context. We find that considering learning can alter the evaluation of alternative policy rules.monetary policy rules; determinacy; expectational stability.

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.