Identifying Elasticities in Autocorrelated Time Series Using Causal Graphs

Silvana Tiedemann, Jorge Sanchez Canales, Felix Schur, Raffaele Sgarlato, Lion Hirth, Oliver Ruhnau and Jonas Peters
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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

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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.

Date: 2024-09
New Economics Papers: this item is included in nep-ecm and nep-ets
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