How to apply the novel dynamic ARDL simulations (dynardl) and Kernel-based regularized least squares (krls)
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 m...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2020-01-01
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| Series: | MethodsX |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215016120303800 |
| _version_ | 1828976175811133440 |
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| author | Samuel Asumadu Sarkodie Phebe Asantewaa Owusu |
| author_facet | Samuel Asumadu Sarkodie Phebe Asantewaa Owusu |
| author_sort | Samuel Asumadu Sarkodie |
| collection | DOAJ |
| description | 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 novel dynamic ARDL Simulations and Kernel-based Regularized Least Squares techniques are useful and improved time series techniques for policy formulation. • We customize ARDL and dynamic simulated ARDL by adding plot estimates with confidence intervals. • A step-by-step procedure of applying ARDL, dynamic ARDL Simulations and Kernel-based Regularized Least Squares is provided. • All techniques are applied to examine the economic effect of denuclearization in Switzerland by 2034. |
| first_indexed | 2024-12-14T14:32:57Z |
| format | Article |
| id | doaj.art-8cb492f966504904a4e51157c5155763 |
| institution | Directory Open Access Journal |
| issn | 2215-0161 |
| language | English |
| last_indexed | 2024-12-14T14:32:57Z |
| publishDate | 2020-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | MethodsX |
| spelling | doaj.art-8cb492f966504904a4e51157c51557632022-12-21T22:57:45ZengElsevierMethodsX2215-01612020-01-017101160How to apply the novel dynamic ARDL simulations (dynardl) and Kernel-based regularized least squares (krls)Samuel Asumadu Sarkodie0Phebe Asantewaa Owusu1Corresponding author.; Nord University Business School (HHN), Post Box 1490, 8049 Bodø, NorwayNord University Business School (HHN), Post Box 1490, 8049 Bodø, NorwayThe 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 novel dynamic ARDL Simulations and Kernel-based Regularized Least Squares techniques are useful and improved time series techniques for policy formulation. • We customize ARDL and dynamic simulated ARDL by adding plot estimates with confidence intervals. • A step-by-step procedure of applying ARDL, dynamic ARDL Simulations and Kernel-based Regularized Least Squares is provided. • All techniques are applied to examine the economic effect of denuclearization in Switzerland by 2034.http://www.sciencedirect.com/science/article/pii/S2215016120303800Dynamic autoregressive distributed lag simulationsKernel-based regularized least squaresResponse surface regressionsAverage marginal effectsPointwise derivativestime series techniques |
| spellingShingle | Samuel Asumadu Sarkodie Phebe Asantewaa Owusu How to apply the novel dynamic ARDL simulations (dynardl) and Kernel-based regularized least squares (krls) MethodsX Dynamic autoregressive distributed lag simulations Kernel-based regularized least squares Response surface regressions Average marginal effects Pointwise derivatives time series techniques |
| title | How to apply the novel dynamic ARDL simulations (dynardl) and Kernel-based regularized least squares (krls) |
| title_full | How to apply the novel dynamic ARDL simulations (dynardl) and Kernel-based regularized least squares (krls) |
| title_fullStr | How to apply the novel dynamic ARDL simulations (dynardl) and Kernel-based regularized least squares (krls) |
| title_full_unstemmed | How to apply the novel dynamic ARDL simulations (dynardl) and Kernel-based regularized least squares (krls) |
| title_short | How to apply the novel dynamic ARDL simulations (dynardl) and Kernel-based regularized least squares (krls) |
| title_sort | how to apply the novel dynamic ardl simulations dynardl and kernel based regularized least squares krls |
| topic | Dynamic autoregressive distributed lag simulations Kernel-based regularized least squares Response surface regressions Average marginal effects Pointwise derivatives time series techniques |
| url | http://www.sciencedirect.com/science/article/pii/S2215016120303800 |
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