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Applying physics to gdp forecasting

Summary:
Models developed for gross domestic product (GDP) growth forecasting tend to be extremely complex, relying on a large number of variables and parameters. Such complexity is not always to the benefit of the accuracy of the forecast. Economic complexity constitutes a framework that builds on methods developed for the study of complex systems to construct approaches that are less demanding than standard macroeconomic ones in terms of data requirements, but whose accuracy remains to be systematically benchmarked. Here we develop a forecasting scheme that is shown to outperform the accuracy of the five-year forecast issued by the International Monetary Fund (IMF) by more than 25% on the available data. The model is based on effectively representing economic growth as a two-dimensional dynamical

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Models developed for gross domestic product (GDP) growth forecasting tend to be extremely complex, relying on a large number of variables and parameters. Such complexity is not always to the benefit of the accuracy of the forecast. Economic complexity constitutes a framework that builds on methods developed for the study of complex systems to construct approaches that are less demanding than standard macroeconomic ones in terms of data requirements, but whose accuracy remains to be systematically benchmarked. Here we develop a forecasting scheme that is shown to outperform the accuracy of the five-year forecast issued by the International Monetary Fund (IMF) by more than 25% on the available data. The model is based on effectively representing economic growth as a two-dimensional dynamical system, defined by GDP per capita and ‘fitness’, a variable computed using only publicly available product-level export data. We show that forecasting errors produced by the method are generally predictable and are also uncorrelated to IMF errors, suggesting that our method is extracting information that is complementary to standard approaches. We believe that our findings are of a very general nature and we plan to extend our validations on larger datasets in future works.

That is from A. Tacchella, D. Mazzilli, and L Pietronero in Nature.  Here is a Chris Lee story about the piece.  Via John Chamberlin.

The post Applying physics to gdp forecasting appeared first on Marginal REVOLUTION.

Tyler Cowen
Tyler Cowen is an American economist, academic, and writer. He occupies the Holbert C. Harris Chair of economics as a professor at George Mason University and is co-author, with Alex Tabarrok, of the popular economics blog Marginal Revolution. Cowen and Tabarrok have also ventured into online education by starting Marginal Revolution University. He currently writes the "Economic Scene" column for the New York Times, and he also writes for such publications as The New Republic, the Wall Street Journal, Forbes, Newsweek, and the Wilson Quarterly.

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