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Identifying Elasticities in Autocorrelated Time Series Using Causal Graphs

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Listed:
  • Silvana Tiedemann

    (Centre for Sustainability, Hertie School)

  • Jorge Sanchez Canales

    (Centre for Sustainability, Hertie School)

  • Felix Schur

    (Department of Mathematics, ETH Zurich)

  • Raffaele Sgarlato

    (Centre for Sustainability, Hertie School)

  • Lion Hirth

    (Centre for Sustainability, Hertie School)

  • Oliver Ruhnau

    (Department of Economics and Institute of Energy Economics, University of Cologne)

  • Jonas Peters

    (Department of Mathematics, ETH Zurich)

Abstract

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.

Suggested Citation

  • Silvana Tiedemann & Jorge Sanchez Canales & Felix Schur & Raffaele Sgarlato & Lion Hirth & Oliver Ruhnau & Jonas Peters, 2024. "Identifying Elasticities in Autocorrelated Time Series Using Causal Graphs," Papers 2409.15530, arXiv.org.
  • Handle: RePEc:arx:papers:2409.15530
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    References listed on IDEAS

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    1. Maniatis, Georgios I. & Milonas, Nikolaos T., 2022. "The impact of wind and solar power generation on the level and volatility of wholesale electricity prices in Greece," Energy Policy, Elsevier, vol. 170(C).
    2. José Luis Montiel Olea & Mikkel Plagborg-Møller & Eric Qian & Christian K. Wolf, 2024. "Double Robustness of Local Projections and Some Unpleasant VARithmetic," NBER Working Papers 32495, National Bureau of Economic Research, Inc.
    3. Knaut, Andreas & Paulus, Simon, 2016. "When are consumers responding to electricity prices? An hourly pattern of demand elasticity," EWI Working Papers 2016-7, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI), revised 16 Mar 2017.
    4. Thomas Richardson, 2003. "Markov Properties for Acyclic Directed Mixed Graphs," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 30(1), pages 145-157, March.
    5. J. Tinbergen, 1940. "Econometric Business Cycle Research," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 7(2), pages 73-90.
    6. Valerie A. Ramey & Sarah Zubairy, 2018. "Government Spending Multipliers in Good Times and in Bad: Evidence from US Historical Data," Journal of Political Economy, University of Chicago Press, vol. 126(2), pages 850-901.
    7. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, November.
    8. Donald B. Rubin, 2005. "Causal Inference Using Potential Outcomes: Design, Modeling, Decisions," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 322-331, March.
    9. Webel, Karsten, 2022. "A review of some recent developments in the modelling and seasonal adjustment of infra-monthly time series," Discussion Papers 31/2022, Deutsche Bundesbank.
    10. José Luis Montiel Olea & Mikkel Plagborg‐Møller, 2021. "Local Projection Inference Is Simpler and More Robust Than You Think," Econometrica, Econometric Society, vol. 89(4), pages 1789-1823, July.
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