Séminaires

Are policymakers ambiguity averse?

Economie et Sciences de la décision

Intervenant : Professeur Loïc Berger
IESEG

26 octobre 2017 - HEC Campus - Bâtiment T - Salle T017 - De 14h00 à 15h00


We investigate the ambiguity preferences of a unique sample of real-life policymakers at the Paris UN climate conference (COP21). We find that policymakers are generally ambiguity averse. Using a simple design which explicitly makes the distinction between objective and subjective probabilities presented in different layers, we are moreover able to detect a strong association between preferences towards model uncertainty and those towards ambiguity. These results suggest that the preferences policymakers exhibit towards ambiguity are not necessarily due to an irrational behavior (such as the inability to reduce compound lotteries), but rather to intrinsic preferences over unknown probabilities, thus shedding new light on the role that ambiguity models can play in informing policymaking. Results are confirmed in a laboratory experiment with university students.

Ambiguous Policy Announcements

Economie et Sciences de la décision

Intervenant : Luigi Paciello
Professeur , EIEF

12 octobre 2017 - HEC Campus - Bâtiment T - Salle T004 - De 14h00 à 15h00


We study the effects of an announcement of a future shift in monetary policy in a new Keynesian model, where ambiguity-averse households with heterogeneous net financial wealth face Knightian uncertainty about the credibility of the announcement. The response of aggregate demand to the announcement of a future loosening in monetary policy falls when financial wealth is more concentrated. The concentration of financial wealth matters because households with great net financial wealth (creditors) are those who are the most likely to believe the announcement, due to the potential loss of wealth from the prospective policy easing. And when creditors believe the announcement more than debtors, their expected wealth losses are larger than the wealth gains that debtors expect. So aggregate net wealth is perceived to fall, which attenuates the effects of forward guidance announcements and can even lead to a contraction in aggregate demand when financial wealth is concentrated enough. We calibrate the model to the Euro area after allowing agents to trade in short and long term nominal assets as well in real assets, and find that the effect can be quantitatively important.

The Comparative Advantage of Cities

Economie et Sciences de la décision

Intervenant : Pr Donald Davis
Columbia

21 septembre 2017 - Campus HEC - Bâtiment T - salle T020 - De 13h30 à 14h30


What determines the distributions of skills, occupations, and industries across cities? We develop a theory to jointly address these fundamental questions about the spatial organization of economies. Our model incorporates a system of cities, their internal urban structures, and a high-dimensional theory of factor-driven comparative advantage. It predicts that larger cities will be skill-abundant and specialize in skillintensive activities according to the monotone likelihood ratio property. We test the model using data on 270 US metropolitan areas, 3 to 9 educational categories, 22 occupations, and 19 industries. The results provide support for our theory’s predictions.

Economie et Sciences de la décision

Intervenant : Michele Tertilt
University of Mannheim

15 juin 2017


The wicked learning environment of regression toward the mean

Economie et Sciences de la décision

Intervenant : Emre Soyer
Ozyegin

4 mai 2017 - T017 - De 13h30 à 14h30

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The environment in which people experience regression toward the mean inhibits accurate learning and valid intuitions in many domains, including medicine, sports and management. In predictive tasks, regression effects are only salient in rare cases where cues take extreme values. People often experience regression away from the mean. Furthermore, errors from predictions that ignore regression effects correlate highly with those of optimal predictions. In diagnostic tasks, people fail to recognize regression effects because they are motivated to seek causal explanations. Causes are attributed to easily identifiable factors that make good stories. A simple heuristic can overcome these inferential difficulties. In predictive tasks, a “50/50 rule” that gives equal weight to the cue and the mean of the target variable approximates optimal performance. In diagnostic tasks, the same rule can be used to generate non-causal counterfactuals to challenge possible causal candidates.


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