{"content":[{"text":"Uncertainty comes in many forms both in the models economists build and analyze \n and in the real world. The models that we as economists build and analyze are highly \n stylized. The include “shocks,” random impulses such as technology shocks or monetary \n policy shocks that are transmitted over time. They are at best approximations because \n they ignore some forces and oversimplify others. (Put impolitely, our models are “wrong.”) \n In addition, we typically have multiple models at our disposal and are unsure how much \n credibility to assign to each. Data helps us to make this assessment, but evidence is typi- \n cally only partially informative. While these challenges are familiar to applied researchers, \n similar challenges confront any decision maker who faces uncertainty including investors or \n policy makers. Individuals or decision-making entities have views or perspectives about the \n future that shape their actions. These views are necessary simpliﬁcations, and there may \n be multiple possible perspectives to be considered. The more complex the underlying envi- \n ronment is, the bigger the challenge for how best to confront the uncertainty in meaningful \n ways. Thus, when there are more environmental intricacies, there is greater scope of a \n broader approach to uncertainty that is important. Recent dynamic decision theory under \n uncertainty and a related control theory aim to provide tools to assess uncertainty broadly \n conceived. The research displayed in this paper and elsewhere aims to convert abstract \n insights into operational tools of analysis to enhance our understanding of how ﬁnancial \n markets work and to provide more sober assessments of economic policy, or assessments \n that avoid false pretenses of knowledge that are too common in policy discussions. \n This particular paper develops and applies speciﬁc tools to study how uncertainty, \n skepticism, and conﬁdence can impact prices and quantities in ﬁnancial markets. Imagine \n investors who are unsure about the future. They have at their disposal multiple views or \n models of the future, but they are not fully conﬁdent about any of these views. Moreover, \n these investors suspect that the structures of the models that express their views are in some \n ways ﬂawed. The ﬂaws themselves are unknown and thus not easily correctable in the near \n future. This perspective broadens the notion of uncertainty relative to the more familiar risk \n aversion paradigm that dominates economic analysis and discourse. It also adds richness \n to our understanding of the price of uncertainty and ﬂuctuations in this price over time. In \n Prices of Macroeconomic Uncertainties with Tenuous Beliefs, we provide a formal analytical \n structure and oﬀer a quantitative illustration that focuses on macroeconomic growth and \n its persistence. In our example economy, we confront investors with uncertainty about the \n future of macroeconomic growth in a stylized way. This uncertainty is meant to capture, say, \n the possibility but not the certainty of secular stagnation debated by macroeconomists, or","bbox":{"left":0.123529,"top":0.100316,"width":0.753081,"height":0.762079}},{"text":"1","bbox":{"left":0.495217,"top":0.895686,"width":0.009552000000000005,"height":0.015109999999999957}}],"images_bbox":[],"images_bbox_not_text_overlap":[]}