Author(s)

Pegah Mahdavi^1*, Mohammad Ali Ehsani¹, Daniel Felix Ahelegbey², Mehrnaz Mohammadpour³

¹Department of Economic and Administrative Sciences, University of Mazandaran, Babolsar, Iran.
²Department of Economics and Management Sciences, University of Pavia, Pavia, Italy.
³Department of Mathematical Sciences, University of Mazandaran, Babolsar, Iran.

Modeling dynamic systems with linear parametric models usually suffer limitation which affects forecasting performance and policy implications. This paper advances a non-parametric autoregressive distributed lag model that employs a Bayesian additive regression tree (BART). The performance of the BART model is compared with selection models like Lasso, Elastic Net, and Bayesian networks in simulation experiments with linear and non-linear data generating processes (DGP), and on US macroeconomic time series data. The results show that the BART model is quite competitive against the linear parametric methods when the DGP is linear, and outperforms the competing methods when the DGP is non-linear. The empirical results suggest that the BART estimators are generally more efficient than the traditional linear methods when modeling and forecasting macroeconomic time series.

BART Model, Non Parametric Modeling, Machine Learning, Regression Trees, Bayesian Network VAR

Mahdavi, P. , Ehsani, M. , Ahelegbey, D. and Mohammadpour, M. (2024) Measuring Causal Effect with ARDL-BART: A Macroeconomic Application. Applied Mathematics, 15, 292-312. doi: 10.4236/am.2024.154018.