Deus ex Machina A Framework for Macro Forecasting with Machine Learning

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  • Author : Marijn A. Bolhuis
  • Publisher : International Monetary Fund
  • Pages : 25 pages
  • ISBN : 1513531727
  • Rating : /5 from reviews
CLICK HERE TO GET THIS BOOK >>>Deus ex Machina A Framework for Macro Forecasting with Machine Learning

Download or Read online Deus ex Machina A Framework for Macro Forecasting with Machine Learning full in PDF, ePub and kindle. this book written by Marijn A. Bolhuis and published by International Monetary Fund which was released on 28 February 2020 with total page 25 pages. We cannot guarantee that Deus ex Machina A Framework for Macro Forecasting with Machine Learning book is available in the library, click Get Book button and read full online book in your kindle, tablet, IPAD, PC or mobile whenever and wherever You Like. We develop a framework to nowcast (and forecast) economic variables with machine learning techniques. We explain how machine learning methods can address common shortcomings of traditional OLS-based models and use several machine learning models to predict real output growth with lower forecast errors than traditional models. By combining multiple machine learning models into ensembles, we lower forecast errors even further. We also identify measures of variable importance to help improve the transparency of machine learning-based forecasts. Applying the framework to Turkey reduces forecast errors by at least 30 percent relative to traditional models. The framework also better predicts economic volatility, suggesting that machine learning techniques could be an important part of the macro forecasting toolkit of many countries.

Deus ex Machina A Framework for Macro Forecasting with Machine Learning

Deus ex Machina  A Framework for Macro Forecasting with Machine Learning
  • Author : Marijn A. Bolhuis,Brett Rayner
  • Publisher : International Monetary Fund
  • Release : 28 February 2020
GET THIS BOOK Deus ex Machina A Framework for Macro Forecasting with Machine Learning

We develop a framework to nowcast (and forecast) economic variables with machine learning techniques. We explain how machine learning methods can address common shortcomings of traditional OLS-based models and use several machine learning models to predict real output growth with lower forecast errors than traditional models. By combining multiple machine learning models into ensembles, we lower forecast errors even further. We also identify measures of variable importance to help improve the transparency of machine learning-based forecasts. Applying the framework to

Issues in Mechanical Engineering 2011 Edition

Issues in Mechanical Engineering  2011 Edition
  • Author : Anonim
  • Publisher : ScholarlyEditions
  • Release : 09 January 2012
GET THIS BOOK Issues in Mechanical Engineering 2011 Edition

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