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577 Results

Ten Isn’t Large! Group Size and Coordination in a Large-Scale Experiment

Economic activities typically involve coordination among a large number of agents. These agents have to anticipate what other agents think before making their own decisions.

What Fed Funds Futures Tell Us About Monetary Policy Uncertainty

Staff working paper 2016-61 Jean-Sébastien Fontaine
The uncertainty around future changes to the Federal Reserve target rate varies over time. In our results, the main driver of uncertainty is a “path” factor signaling information about future policy actions, which is filtered from federal funds futures data.

Estimating the Effect of Exchange Rate Changes on Total Exports

Staff working paper 2019-17 Thierry Mayer, Walter Steingress
This paper shows that real effective exchange rate (REER) regressions, the standard approach for estimating the response of aggregate exports to exchange rate changes, imply biased estimates of the underlying elasticities. We provide a new aggregate regression specification that is consistent with bilateral trade flows micro-founded by the gravity equation.

The Anatomy of Sentiment-Driven Fluctuations

Staff working paper 2021-33 Sushant Acharya, Jess Benhabib, Zhen Huo
We show that changes in sentiment that aren’t related to fundamentals can drive persistent macroeconomic fluctuations even when all economic agents are rational. Changes in sentiment can also affect how fundamental shocks affect macroeconomic outcomes.

Composite Likelihood Estimation of an Autoregressive Panel Probit Model with Random Effects

Staff working paper 2019-16 Kerem Tuzcuoglu
Modeling and estimating persistent discrete data can be challenging. In this paper, we use an autoregressive panel probit model where the autocorrelation in the discrete variable is driven by the autocorrelation in the latent variable. In such a non-linear model, the autocorrelation in an unobserved variable results in an intractable likelihood containing high-dimensional integrals.

Predictive Density Combination Using a Tree-Based Synthesis Function

This paper studies non-parametric combinations of density forecasts. We introduce a regression tree-based approach that allows combination weights to vary on the features of the densities, time-trends or economic indicators. In two empirical applications, we show the benefits of this approach in terms of improved forecast accuracy and interpretability.
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