How to apply the novel dynamic ARDL simulations (dynardl) and Kernel-based regularized least squares (krls).

ABSTRACT: The application of dynamic Autoregressive Distributed Lag (dynardl) simulations and Kernel-based Regularized Least Squares (krls) to time series data is gradually gaining recognition in energy, environmental and health economics. The Kernel-based Regularized Least Squares technique is a simplified machine learning-based algorithm with strength in its interpretation and accounting for heterogeneity, additivity and nonlinear effects. The novel dynamic ARDL Simulations algorithm is useful for testing cointegration, long and short-run equilibrium relationships in both levels and differences. Advantageously, the novel dynamic ARDL Simulations has visualization interface to examine the possible counterfactual change in the desired variable based on the notion of ceteris paribus. Thus, the

SUBMITTER: Sarkodie SA 

PROVIDER: S-EPMC7718150 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature