Abstract

This paper provides a guide for using causal inference with asset prices and quantities. Our framework revolves around an elementary assumption about portfolio demand: homogeneous substitution conditional on observables. Under this assumption, standard cross-sectional instrumental variables or difference-in-difference regressions identify the relative demand elasticity between assets with the same observables, the difference between own-price and cross-price elasticity. In contrast, identifying aggregate elasticities and substitution along specific characteristics requires joint estimation using multiple sources of exogenous time-series variation. The same principles apply to the estimation of multipliers measuring the price impact of supply or demand shocks. Our assumption maps to familiar restrictions on covariance matrices in classical asset pricing models, encompass demand models such as logit, and accommodate rich substitution patterns even outside of these models. We discuss how to design experiments satisfying this condition and offer diagnostics to validate it.

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