Silvana Tiedemann, Jorge Sanchez Canales, Felix Schur, Raffaele Sgarlato, Lion Hirth, Oliver Ruhnau, Jonas Peters
arXiv 23 Sep 2024 · Econometrics · 1 citations (OpenAlex)
arXiv:2409.15530 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Pearl, J (2009) Causality: Models, Reasoning, and Inference | 1.000 | 10 | 4 | 100% |
| 2 | Thams, N., Søndergaard, R., Weichwald, S., Peters, J (2022) Identifying Causal Effects using Instrumental Time Series: Nuisance IV and Correcting for the Past self | 1.000 | 9 | 5 | 100% |
| 3 | Lauritzen, S. L (1996) Graphical Models | 0.928 | 4 | 4 | 100% |
| 4 | Peters, J., Janzing, D., Schölkopf, B (2013) Causal Inference on Time Series using Structural Equation Models self | 0.928 | 4 | 4 | 100% |
| 5 | Montiel Olea, J.L., Plagborg-Møller, M (2021) Local Projection Inference Is Simpler and More Robust Than You Think | 0.928 | 4 | 3 | 100% |
| 6 | Montiel Olea, J.L., Plagborg-Møller, M., Qian, E., Wolf, C.K (2024) Double Robustness of Local Projections and Some Unpleasant VARithmetic | 0.811 | 4 | 2 | 100% |
| 7 | Spirtes, P., Glymour, C., Scheines, R (2000) Causation, Prediction, and Search | 0.811 | 4 | 2 | 100% |
| 8 | Stock, J. H., Watson, M. W (2018) Identification and Estimation of Dynamic Causal Effects in Macroeconomics Using External Instruments | 0.811 | 4 | 2 | 100% |
| 9 | Henckel, L., Buttenschoen, M., Maathuis, M. H (2023) Graphical tools for selecting conditional instrumental sets | 0.737 | 3 | 2 | 100% |
| 10 | Plagborg-Møller, M., Wolf, C. K (2021) Local Projections and VARs Estimate the Same Impulse Responses | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 59 scored citations.