Machine Learning Regularization Methods in High-Dimensional Monetary and Financial VARs
Vector autoregressions (VARs) and their multiple variants are standard models in economic and financial research due to their power for forecasting, data analysis and inference. These properties are a consequence of their capabilities to include multiple variables and lags which, however, turns into...
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| Format: | info:eu-repo/semantics/article |
| Language: | English |
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MDPI
2022
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| Online Access: | http://hdl.handle.net/10835/13536 |
| _version_ | 1789406293270724608 |
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| author | Sánchez García, Javier Cruz Rambaud, Salvador |
| author_facet | Sánchez García, Javier Cruz Rambaud, Salvador |
| author_sort | Sánchez García, Javier |
| collection | DSpace |
| description | Vector autoregressions (VARs) and their multiple variants are standard models in economic and financial research due to their power for forecasting, data analysis and inference. These properties are a consequence of their capabilities to include multiple variables and lags which, however, turns into an exponential growth of the parameters to be estimated. This means that high-dimensional models with multiple variables and lags are difficult to estimate, leading to omitted variables, information biases and a loss of potential forecasting power. Traditionally, the existing literature has resorted to factor analysis, and specially, to Bayesian methods to overcome this situation. This paper explores the so-called machine learning regularization methods as an alternative to traditional methods of forecasting and impulse response analysis. We find that regularization structures, which allow for high dimensional models, perform better than standard Bayesian methods in nowcasting and forecasting. Moreover, impulse response analysis is robust and consistent with economic theory and evidence, and with the different regularization structures. Specifically, regarding the best regularization structure, an elementwise machine learning structure performs better in nowcasting and in computational efficiency, whilst a componentwise structure performs better in forecasting and cross-validation methods. |
| format | info:eu-repo/semantics/article |
| id | oai:repositorio.ual.es:10835-13536 |
| institution | Universidad de Cuenca |
| language | English |
| publishDate | 2022 |
| publisher | MDPI |
| record_format | dspace |
| spelling | oai:repositorio.ual.es:10835-135362023-04-12T19:04:48Z Machine Learning Regularization Methods in High-Dimensional Monetary and Financial VARs Sánchez García, Javier Cruz Rambaud, Salvador VAR machine learning LASSO (Least Absolute Shrinkage and Selection Operator) regularization methods sparsity monetary economics financial economics Vector autoregressions (VARs) and their multiple variants are standard models in economic and financial research due to their power for forecasting, data analysis and inference. These properties are a consequence of their capabilities to include multiple variables and lags which, however, turns into an exponential growth of the parameters to be estimated. This means that high-dimensional models with multiple variables and lags are difficult to estimate, leading to omitted variables, information biases and a loss of potential forecasting power. Traditionally, the existing literature has resorted to factor analysis, and specially, to Bayesian methods to overcome this situation. This paper explores the so-called machine learning regularization methods as an alternative to traditional methods of forecasting and impulse response analysis. We find that regularization structures, which allow for high dimensional models, perform better than standard Bayesian methods in nowcasting and forecasting. Moreover, impulse response analysis is robust and consistent with economic theory and evidence, and with the different regularization structures. Specifically, regarding the best regularization structure, an elementwise machine learning structure performs better in nowcasting and in computational efficiency, whilst a componentwise structure performs better in forecasting and cross-validation methods. 2022-03-23T17:21:02Z 2022-03-23T17:21:02Z 2022-03-10 info:eu-repo/semantics/article 2227-7390 http://hdl.handle.net/10835/13536 10.3390/math10060877 en https://www.mdpi.com/2227-7390/10/6/877 Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess MDPI |
| spellingShingle | VAR machine learning LASSO (Least Absolute Shrinkage and Selection Operator) regularization methods sparsity monetary economics financial economics Sánchez García, Javier Cruz Rambaud, Salvador Machine Learning Regularization Methods in High-Dimensional Monetary and Financial VARs |
| title | Machine Learning Regularization Methods in High-Dimensional Monetary and Financial VARs |
| title_full | Machine Learning Regularization Methods in High-Dimensional Monetary and Financial VARs |
| title_fullStr | Machine Learning Regularization Methods in High-Dimensional Monetary and Financial VARs |
| title_full_unstemmed | Machine Learning Regularization Methods in High-Dimensional Monetary and Financial VARs |
| title_short | Machine Learning Regularization Methods in High-Dimensional Monetary and Financial VARs |
| title_sort | machine learning regularization methods in high-dimensional monetary and financial vars |
| topic | VAR machine learning LASSO (Least Absolute Shrinkage and Selection Operator) regularization methods sparsity monetary economics financial economics |
| url | http://hdl.handle.net/10835/13536 |
| work_keys_str_mv | AT sanchezgarciajavier machinelearningregularizationmethodsinhighdimensionalmonetaryandfinancialvars AT cruzrambaudsalvador machinelearningregularizationmethodsinhighdimensionalmonetaryandfinancialvars |