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We study the properties of generalized stochastic gradient (GSG) learning in forward-looking models. We examine how the conditions for stability of standard stochastic gradient (SG) learning both differ from and are related to E-stability, which governs stability under least squares learning. SG algorithms are sensitive to units of measurement and we show that there is a transformation of variables for which E-stability governs SG stability. GSG algorithms with constant gain have a deeper justification in terms of parameter drift, robustness and risk sensitivity.

L'hypothèse de référence, celle des anticipations rationnelles, suppose, d'une part, un degré irréaliste de rationalité des agents économiques, et d'autre part élude la question de savoir comment les agents parviennent à se coordonner sur une position d'équilibre. Cet article examine comment les théories de l'apprentissage, et plus particulièrement de l'apprentissage adaptatif, traitent ces questions et peuvent conduire à des conclusions en termes de politique économique distinctes de celles obtenues avec anticipations rationnelles. Des exemples sont discutés, ils incluent la politique monétaire au sein de modèles de la nouvelle économie keynésienne, la vision néo-Fisherienne de la politique économique, les objectifs d'inflation, les modèles d'hyperinflation et les politiques macroéconomiques visant à éviter la stagnation au taux plancher zéro.

A crucial challenge for economists is figuring out how people interpret the world and form expectations that will likely influence their economic activity. Inflation, asset prices, exchange rates, investment, and consumption are just some of the economic variables that are largely explained by expectations. Here George Evans and Seppo Honkapohja bring new explanatory power to a variety of expectation formation models by focusing on the learning factor. Whereas the rational expectations paradigm offers the prevailing method to determining expectations, it assumes very theoretical knowledge o

A fundamentals based monetary policy rule, which would be the optimal monetary policy without commitment when private agents have perfectly rational expectations, is unstable if in fact these agents follow standard adaptive learning rules. This problem can be overcome if private expectations are observed and suitably incorporated into the policy maker's optimal rule. These strong results extend to the case in which there is simultaneous learning by the policy maker and the private agents. Our findings show the importance of conditioning policy appropriately, not just on fundamentals, but also directly on observed household and firm expectations.