A Forward-Looking Approach to Learning in Macroeconomic Models∗

This paper illustrates how learning can be incorporated into an existing forward-looking macroeconomic model as an alternative to the more conventional but arguably more extreme assumption of model consistent or rational expectations. The key characteristic of the model consistent learning approach to be adopted here is that agents are assumed to know the true structure of the model but that they need to learn about some parameters of that system, for example those defining the government's policy decision rule. Importantly, models solved under this assumption retain the property that the current behaviour of economic agents can be influenced by the expected future effects of policy changes. This type of learning may be contrasted with one where economic agents may also be uncertain about some structural parameters of the true model but in addition, they do not possess sufficient information to form future expectations consistent with their estimated model. As a consequence, expectations are formed using backward-looking reduced form equations with parameters which agents continuously learn about. This approach, known as boundedly rational learning, has been adopted in Hall and Garratt (1992) who apply these techniques to a full-scale non-linear macroeconometric model.