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Working Paper

Working Paper


Working Papers (Click to download)

Works in Progress

Forecast Inflation with Disaggregated Data and Machine Learning (with Jeremy Piger)

We use machine learning techniques common in artificial intelligence literature to forecast the inflation rate with disaggregated data. We found that machine learning techniques have better forecasting performance than any traditional methods including autoregressive models, random walk models, and dynamic factor models. This work can help policymakers to form more accurate forecasts about some aggregate economic variable when they have access to the disaggregated data.  

E-stability Principle with Social Learning (with George Evans and Bruce McGough)

We show with simulations that E-stability principle holds in a reduced-form New Keynesian model where agents hold different beliefs and update through genetic learning algorithm. In addition, we provide the details for how each fundamental parameter affects the speed of convergence.