Neural Network Modeling and What-If Scenarios: Applications for Market Development Forecasting

Valentina Kuskova (), Dmitry Zaytsev, Gregory Khvatsky () and Anna Sokol ()
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Valentina Kuskova: University of Notre Dame
Dmitry Zaytsev: University of Notre Dame at Tantur
Gregory Khvatsky: University of Notre Dame
Anna Sokol: University of Notre Dame

A chapter in Applications in Reliability and Statistical Computing, 2023, pp 271-288 from Springer

Abstract: Abstract In this chapter, we demonstrate the use of neural networks for forecasting automotive market sales. Using data from an entire country, with a large set of micro- and macroeconomic variables and other factors, we show how to supplement “black box” neural network calculations with a solid theoretical foundation from social sciences. Doing so allows to not only create exceptionally accurate forecasts, but understand the “black box” weights on different variables that are used in generating predictions. We also demonstrate how individual variables fit into the overall market dynamics, and how political changes play a role in economic outcomes.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:ssrchp:978-3-031-21232-1_14

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DOI: 10.1007/978-3-031-21232-1_14

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