Suchergebnisse

The CPI component indices are obtained from comparing price quotes at a given store in different periods. If we omit comparisons from goods in the store in the initial, but not in the comparison, period we generate a selection bias: goods that exit are disproportionately obsolete goods that have falling prices. Building on Pakes (2003), we explain why standard hedonic predictions for second-period prices of exiting goods do not account for this bias. New hedonic methods are derived, shown to have desirable properties, and are applied to three CPI samples where they generate significant selection corrections. (JEL C43, E31)

Today, diagnostic reasoning combines common and specialized knowledge, elements of numeracy, some facility with the basics of probability theory and, last but not least, ease in interactions with AI tools. We present procedures and tools for constructing trees that lead to understandable, transparent, simple, and robust classifications and decisions. These tools are more heuristic than optimal models, inspired by the perspective of Bounded Rationality. We describe how the tenets of Bounded Rationality provide a framework for the human-machine interaction this paper is devoted to. We claim that, because of this rationality, our proposed tools facilitate machine-aided decision making that is smooth, transparent and successful.