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

Area of Science:

• Econometrics
• Time Series Analysis
• Machine Learning Applications in Economics

Background:

• Dynamic Autoregressive Distributed Lag (dynardl) simulations and Kernel-based Regularized Least Squares (krls) are increasingly recognized in energy, environmental, and health economics.
• Kernel-based Regularized Least Squares (krls) is a machine learning algorithm noted for its interpretability and ability to handle heterogeneity, additivity, and nonlinear effects.
• The dynamic ARDL Simulations algorithm is valuable for testing cointegration and long/short-run equilibrium relationships in time series data.

Purpose of the Study:

• To present customized Autoregressive Distributed Lag (ARDL) and dynamic ARDL simulations with added plot estimates and confidence intervals.
• To provide a step-by-step guide for applying ARDL, dynamic ARDL Simulations, and Kernel-based Regularized Least Squares (krls).
• To examine the economic impact of Switzerland's denuclearization by 2034 using these advanced time series techniques.

Main Methods:

• Application of Kernel-based Regularized Least Squares (krls) for its interpretative strengths and handling of complex data structures.
• Utilization of dynamic Autoregressive Distributed Lag (dynardl) Simulations for cointegration testing and equilibrium relationship analysis.
• Customization of ARDL and dynamic ARDL simulations to include confidence intervals for enhanced visualization of counterfactual scenarios.

Main Results:

• The study demonstrates the practical application of dynardl simulations and krls for economic policy analysis.
• Customized ARDL and dynamic ARDL simulations provide visual insights into potential economic changes under a ceteris paribus assumption.
• The techniques are successfully applied to assess the economic consequences of Switzerland's planned denuclearization by 2034.

Conclusions:

• Dynamic ARDL Simulations and Kernel-based Regularized Least Squares (krls) represent improved time series techniques beneficial for policy formulation.
• The integration of visualization tools enhances the understanding of counterfactual economic scenarios.
• These methods offer robust analytical capabilities for complex economic questions, such as energy policy transitions.