Causality in Structural Vector Autoregressions: Science or Sorcery?. Ghanem, D. & Smith, A. American Journal of Agricultural Economics, 104:881-904, 2022. ![Causality in Structural Vector Autoregressions: Science or Sorcery? [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex 40 downloads This paper presents the structural vector autoregression (SVAR) as a method for estimating dynamic causal effects in agricultural and resource economics. Our paper has a pedagogical purpose; we aim the presentation at economists trained primarily in microeconometrics. We emphasize connections between SVARs and the classical instrumental variables (IV) model, both of which aim to extract exogenous variation from endogenous variables. We show that the population analogue of the Wald IV estimator is identical to the ratio of two impulse responses from an SVAR. We present an SVAR analysis of global supply and demand for agricultural commodities, which was previously examined using IV Roberts and Schlenker (2013). We illustrate the additional economic insights that the SVAR reveals. We estimate that demand responds similarly to one-year and longer-run supply changes, whereas supply responds differently depending on whether a price change is driven by poor weather last year or a jump in consumption demand. We highlight the assumptions required to gain these insights and illustrate the robustness of our results to alternative assumptions.
@article{ghanem2022causality,
title={Causality in Structural Vector Autoregressions: Science or Sorcery?},
author={Ghanem, Dalia and Smith, Aaron},
journal={American Journal of Agricultural Economics},
year={2022},
volume={104},
number={},
pages={881-904},
keywords={econometrics},
abstract={This paper presents the structural vector autoregression (SVAR) as a method for estimating dynamic causal effects in agricultural and resource economics. Our paper has a pedagogical purpose; we aim the presentation at economists trained primarily in microeconometrics. We emphasize connections between SVARs and the classical instrumental variables (IV) model, both of which aim to extract exogenous variation from endogenous variables. We show that the population analogue of the Wald IV estimator is identical to the ratio of two impulse responses from an SVAR. We present an SVAR analysis of global supply and demand for agricultural commodities, which was previously examined using IV Roberts and Schlenker (2013). We illustrate the additional economic insights that the SVAR reveals. We estimate that demand responds similarly to one-year and longer-run supply changes, whereas supply responds differently depending on whether a price change is driven by poor weather last year or a jump in consumption demand. We highlight the assumptions required to gain these insights and illustrate the robustness of our results to alternative assumptions.},
url={https://asmith.ucdavis.edu/research/causality-structural-vector-autoregressions-science-or-sorcery}
}
Downloads: 40
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