In response to the recent global economic crisis, governments around the world implemented fiscal stimulus policies to support economic activity. In many advanced economies, this contributed to a large increase in public debt and led to a noticeable shift from stimulus policies towards public debt reduction policies.

What are the macroeconomic effects of these policies? How effective are different types of debt reduction policies in bringing down public debt levels, without dragging down economic growth? And which stimulus programmes are an effective tool to support economic activity?

It is well understood that the policy effects depend crucially on the expectations of households and firms. Lab experiments have shown that their expectations are best described by simple forecasting rules based on limited information, rather than the full-information “rational” rules that are assumed in traditional economic models.

This proposal puts forward the adaptive learning approach to adequately account for private-sector expectations. This approach is a flexible way of combining the aforementioned experimental evidence, insights from cognitive scientists, and macroeconomic theory on fiscal policy into a single analytical framework.

The research proposal contributes to the literature by checking the empirical validity of this framework for fiscal policy analysis. It will also guide policy makers in choosing among alternative policy options.