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Economic growth dynamics: a machine learning-augmented nonlinear autoregressive distributed lag model of asymmetric effect

Year: 2025

Type: article

Abstract: Breaking away from traditional methods, this study investigates the asymmetric effects of macroeconomic shocks on economic growth in Nigeria using a novel Machine Learning-Augmented Nonlinear Autoregressive Distributed Lag (NARDL) model. We utilized a Random Forest algorithm to data-driven feature selection, thus model optimization and its enhanced robustness against random selection of lags. The results confirm the presence of a long-run cointegrating relationship and show that positive and negative oil price shocks statistically differ in their effects on GDP. We also find inflation to have a strong negative long-run effect, and government capital expenditure is a significant driver of growth. Such embedding of machine learning in the NARDL model is a more empirically valid policy analysis tool that supplies key findings for policymakers in resource-dependent economies.

Source: Innovation in Computer and Data Sciences

DOI: 10.64389/icds.2025.01125

Authors: Chrisogonus K. Onyekwere, Chinedu K. Nwankwo, John Abonongo, Emmanuel Chibuogu Asogwa, Anum Shafiq

Institutions: Nnamdi Azikiwe University, University of Abuja, University of Technology and Applied Sciences, VSB - Technical University of Ostrava

Language: English

Topic: Monetary Policy and Economic Impact

Subfield: General Economics, Econometrics and Finance

Field: Economics, Econometrics and Finance

Domain: Social Sciences

SDG: Decent work and economic growth

Open Access status: hybrid

OpenAlex ID: W4414107366

FWCI: 8.082

Cites:

Cited by: 4

Related to: 10