Replication Materials - Assumption Errors and Forecast Accuracy: A Partial Linear Instrumental Variable and Double Machine Learning Approach
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2025
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| _version_ | 1866901415810564096 |
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| author | Schult, Christoph Heinisch, Katja Scaramella, Fabio |
| author_facet | Schult, Christoph Heinisch, Katja Scaramella, Fabio |
| contents | <div> <p>This repository contains the replication files for the paper <em>“Assumption Errors and Forecast Accuracy: A Partial Linear Instrumental Variable and Double Machine Learning Approach”</em>. The study investigates how conditioning variable forecast errors (oil prices, world trade, exchange rates) affect GDP forecast accuracy, using a novel econometric framework based on Partial Linear Instrumental Variables (PLIV) and Double Machine Learning (DML).</p> <p>The replication package includes:</p> <ul> <li> <p><strong>Data</strong>: Forecast and conditioning assumption datasets (1992–2019) covering German GDP forecasts from major national and international institutions.</p> </li> <li> <p><strong>Code</strong>: R scripts implementing OLS, 2SLS, and PLIV-DML estimations using the <code>DoubleML</code> and <code>mlr3</code> packages.</p> </li> <li> <p><strong>Figures and Tables</strong>: Scripts to reproduce all figures and tables reported in the manuscript (main text and appendix).</p> </li> <li> <p><strong>Instructions</strong>: A README file describing how to run the code, install dependencies, and reproduce results.</p> </li> </ul> </div> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17035188 |
| institution | Zenodo |
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| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Replication Materials - Assumption Errors and Forecast Accuracy: A Partial Linear Instrumental Variable and Double Machine Learning Approach Schult, Christoph Heinisch, Katja Scaramella, Fabio <div> <p>This repository contains the replication files for the paper <em>“Assumption Errors and Forecast Accuracy: A Partial Linear Instrumental Variable and Double Machine Learning Approach”</em>. The study investigates how conditioning variable forecast errors (oil prices, world trade, exchange rates) affect GDP forecast accuracy, using a novel econometric framework based on Partial Linear Instrumental Variables (PLIV) and Double Machine Learning (DML).</p> <p>The replication package includes:</p> <ul> <li> <p><strong>Data</strong>: Forecast and conditioning assumption datasets (1992–2019) covering German GDP forecasts from major national and international institutions.</p> </li> <li> <p><strong>Code</strong>: R scripts implementing OLS, 2SLS, and PLIV-DML estimations using the <code>DoubleML</code> and <code>mlr3</code> packages.</p> </li> <li> <p><strong>Figures and Tables</strong>: Scripts to reproduce all figures and tables reported in the manuscript (main text and appendix).</p> </li> <li> <p><strong>Instructions</strong>: A README file describing how to run the code, install dependencies, and reproduce results.</p> </li> </ul> </div> |
| title | Replication Materials - Assumption Errors and Forecast Accuracy: A Partial Linear Instrumental Variable and Double Machine Learning Approach |
| url | https://doi.org/10.5281/zenodo.17035188 |