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76 Results

Model Uncertainty and Wealth Distribution

Staff working paper 2019-48 Edouard Djeutem, Shaofeng Xu
This paper studies the implications of model uncertainty for wealth distribution in a tractable general equilibrium model with a borrowing constraint and robustness à la Hansen and Sargent (2008). Households confront model uncertainty about the process driving the return of the risky asset, and they choose robust policies.

Are Long-Horizon Expectations (De-)Stabilizing? Theory and Experiments

Staff working paper 2019-27 George Evans, Cars Hommes, Isabelle Salle, Bruce McGough
Most models in finance assume that agents make trading plans over the infinite future. We consider instead that they are boundedly rational and may only form forecasts over a limited horizon.

Survival Analysis of Bank Note Circulation: Fitness, Network Structure and Machine Learning

Staff working paper 2020-33 Diego Rojas, Juan Estrada, Kim Huynh, David T. Jacho-Chávez
Using the Bank of Canada's Currency Information Management Strategy, we analyze the network structure traced by a bank note’s travel in circulation and find that the denomination of the bank note is important in our potential understanding of the demand and use of cash.

No Double Standards: Quantifying the Impact of Standard Harmonization on Trade

Staff working paper 2019-36 Julia Schmidt, Walter Steingress
Product standards are omnipresent in industrialized societies. Though standardization can be beneficial for domestic producers, divergent product standards have been categorized as a major obstacle to international trade. This paper quantifies the effect of standard harmonization on trade flows and characterizes the extent to which it changes the cost and demand structure of exporting.

Combining Large Numbers of Density Predictions with Bayesian Predictive Synthesis

Staff working paper 2023-45 Tony Chernis
I show how to combine large numbers of forecasts using several approaches within the framework of a Bayesian predictive synthesis. I find techniques that choose and combine a handful of forecasts, known as global-local shrinkage priors, perform best.
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