A multi-level analytical framework for modeling U.S. economic growth

David Enck, Mario Beruvides, Victor Gustavo Tercero-Gómez, Álvaro Cordero-Franco

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Knowledge of the historical and changing state of a countries economic performance as well as internal performance of a company’s key performance metrics are critical to iterative development of strategy development and deployment. This article offers an improvement in methods for monitoring external and internal performance of key performance measures. We specifically address external monitoring related to the economy, however the framework can be applied to other external or internal measures. Research in macroeconomics describes economic performance as a function of key economic health indicators (KEHIs) such as output, unemployment, and inflation with the goal of understanding the underlying drivers of KEHIs in order to help governments, businesses and people make informed decisions regarding strategy development and deployment. The understanding of economic performance through the KEHIs can be broken into the following components: describing historical performance (including current status) and forecasting future values. Models used to: describe and forecast KEHIs can be partitioned into parametric and nonparametric which differ by how they represent reality. Parametric models start with theoretical relationships and let data influence the model parameters. Nonparametric models let the data, from individual or multiple economic series, influence the model selection. The state-of-the-art parametric macro-economic models did not forecast the 2008 recession. This paper suggests a 2-level analytical framework, based on a proposal by Blanchard, that develops a historical understanding of the data as a foundation and builds knowledge with nonparametric models of increasing complexity that can inform parametric modeling efforts, improving the reliability of external and internal monitoring.

Original languageEnglish
Title of host publicationASEM 41st International Annual Conference Proceedings "Leading Organizations through Uncertain Times"
EditorsH. Keathley, J. Enos, M. Parrish
PublisherAmerican Society for Engineering Management
ISBN (Electronic)9780997519594
StatePublished - 2020
Event41st International Annual Conference of the American Society for Engineering Management: Leading Organizations through Uncertain Times - Virtual, Online
Duration: Oct 28 2020Oct 30 2020

Publication series

NameASEM 41st International Annual Conference Proceedings "Leading Organizations through Uncertain Times"

Conference

Conference41st International Annual Conference of the American Society for Engineering Management: Leading Organizations through Uncertain Times
CityVirtual, Online
Period10/28/2010/30/20

Keywords

  • Auto-regressive model
  • Change points
  • Dynamic stochastic general equilibrium models
  • Factor augmented auto-regressive model
  • Forecasting
  • Statistical process monitoring
  • Strategic management

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