Identifying Elasticities in Autocorrelated Time Series Using Causal Graphs
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866910617951010816 |
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| author | Tiedemann, Silvana Canales, Jorge Sanchez Schur, Felix Sgarlato, Raffaele Hirth, Lion Ruhnau, Oliver Peters, Jonas |
| author_facet | Tiedemann, Silvana Canales, Jorge Sanchez Schur, Felix Sgarlato, Raffaele Hirth, Lion Ruhnau, Oliver Peters, Jonas |
| contents | The price elasticity of demand can be estimated from observational data using instrumental variables (IV). However, naive IV estimators may be inconsistent in settings with autocorrelated time series. We argue that causal time graphs can simplify IV identification and help select consistent estimators. To do so, we propose to first model the equilibrium condition by an unobserved confounder, deriving a directed acyclic graph (DAG) while maintaining the assumption of a simultaneous determination of prices and quantities. We then exploit recent advances in graphical inference to derive valid IV estimators, including estimators that achieve consistency by simultaneously estimating nuisance effects. We further argue that observing significant differences between the estimates of presumably valid estimators can help to reject false model assumptions, thereby improving our understanding of underlying economic dynamics. We apply this approach to the German electricity market, estimating the price elasticity of demand on simulated and real-world data. The findings underscore the importance of accounting for structural autocorrelation in IV-based analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_15530 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Identifying Elasticities in Autocorrelated Time Series Using Causal Graphs Tiedemann, Silvana Canales, Jorge Sanchez Schur, Felix Sgarlato, Raffaele Hirth, Lion Ruhnau, Oliver Peters, Jonas Econometrics Methodology Machine Learning The price elasticity of demand can be estimated from observational data using instrumental variables (IV). However, naive IV estimators may be inconsistent in settings with autocorrelated time series. We argue that causal time graphs can simplify IV identification and help select consistent estimators. To do so, we propose to first model the equilibrium condition by an unobserved confounder, deriving a directed acyclic graph (DAG) while maintaining the assumption of a simultaneous determination of prices and quantities. We then exploit recent advances in graphical inference to derive valid IV estimators, including estimators that achieve consistency by simultaneously estimating nuisance effects. We further argue that observing significant differences between the estimates of presumably valid estimators can help to reject false model assumptions, thereby improving our understanding of underlying economic dynamics. We apply this approach to the German electricity market, estimating the price elasticity of demand on simulated and real-world data. The findings underscore the importance of accounting for structural autocorrelation in IV-based analysis. |
| title | Identifying Elasticities in Autocorrelated Time Series Using Causal Graphs |
| topic | Econometrics Methodology Machine Learning |
| url | https://arxiv.org/abs/2409.15530 |