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Jel Classification:C45 

Working Paper
Macroeconomic Indicator Forecasting with Deep Neural Networks

Economic policymaking relies upon accurate forecasts of economic conditions. Current methods for unconditional forecasting are dominated by inherently linear models {{p}} that exhibit model dependence and have high data demands. {{p}} We explore deep neural networks as an {{p}} opportunity to improve upon forecast accuracy with limited data and while remaining agnostic as to {{p}} functional form. We focus on predicting civilian unemployment using models based on four different neural network architectures. Each of these models outperforms bench- mark models at short time horizons. One model, ...
Research Working Paper , Paper RWP 17-11

Working Paper
How Centralized is U.S. Metropolitan Employment?

Centralized employment remains a benchmark stylization of metropolitan land use.To address its empirical relevance, we delineate "central employment zones" (CEZs)- central business districts together with nearby concentrated employment|for 183 metropolitan areas in 2000. To do so, we first subjectively classify which census tracts in a training sample of metros belong to their metro's CEZ and then use a learning algorithm to construct a function that predicts our judgment. {{p}} Applying this prediction function to the full cross section of metros estimates the probability we would judge ...
Research Working Paper , Paper RWP 17-16

Working Paper
Machine Learning, the Treasury Yield Curve and Recession Forecasting

We use machine learning methods to examine the power of Treasury term spreads and other financial market and macroeconomic variables to forecast US recessions, vis-à-vis probit regression. In particular we propose a novel strategy for conducting cross-validation on classifiers trained with macro/financial panel data of low frequency and compare the results to those obtained from standard k-folds cross-validation. Consistent with the existing literature we find that, in the time series setting, forecast accuracy estimates derived from k-folds are biased optimistically, and cross-validation ...
Finance and Economics Discussion Series , Paper 2020-038

Working Paper
Spatial Dependence and Data-Driven Networks of International Banks

This paper computes data-driven correlation networks based on the stock returns of international banks and conducts a comprehensive analysis of their topological properties. We first apply spatial-dependence methods to filter the effects of strong common factors and a thresholding procedure to select the significant bilateral correlations. The analysis of topological characteristics of the resulting correlation networks shows many common features that have been documented in the recent literature but were obtained with private information on banks? exposures. Our analysis validates these ...
Working Papers (Old Series) , Paper 1627

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