Working Papers

Effects of Health Care Policy Uncertainty on Households’ Portfolio Choice
with Robin L Lumsdaine. (Draft)

Abstract. This paper conducts an empirical analysis of the effect of health care policy uncertainty (HCPU) on households’ portfolio choice. A causal identification approach is developed, whose key assumption is the existence of an exogenous variable that shifts responsiveness to HCPU without shifting responsiveness to other macroeconomic time series. Combined with the assumption of risk averse agents, this approach results in an informative bound on the average causal effect of HCPU. The empirical results highlight the importance of HCPU as a determinant of households’ financial behavior, and showcase substantial heterogeneity in HCPU effects across varying unexpected changes to health.

Demand Estimation with Finitely Many Consumers
with Jonas Lieber. (Draft)

Abstract. Although market shares are frequently estimated via averages of finitely many consumer choices, commonly applied methods for demand estimation are not robust to estimation error in these shares. While non-negligible estimation error in market shares always introduces bias in the demand parameter estimators, the issue becomes most salient when estimated market shares are zero. In the presence of zero shares, widely applied estimators of the random coefficient logit model cannot be computed without ad-hoc data manipulations. This paper proposes a new estimator of demand parameters for settings with endogenous prices and estimated market shares that is robust to zero-valued market shares. The estimator generalizes the constrained optimization program of Dubé et al. (2012) with probabilistic bounds on the estimation error in market shares. We show consistency as the number of markets $T$ grows sufficiently slowly relative to the number of consumers $n$ such that $\log(T)/n\to 0$, and provide confidence intervals under the same regime. Simulations suggest improved finite sample properties of the proposed estimator to conventional alternatives.

ddml: Double/debiased machine learning in Stata
with Achim Ahrens, Christian B Hansen, Mark E Schaffer. (arXiv)

Abstract. We introduce the package ddml for Double/Debiased Machine Learning (DDML) in Stata. Estimators of causal parameters for five different econometric models are supported, allowing for flexible estimation of causal effects of endogenous variables in settings with unknown functional forms and/or many exogenous variables. ddml is compatible with many existing supervised machine learning programs in Stata. We recommend using DDML in combination with stacking estimation which combines multiple machine learners into a final predictor. We provide Monte Carlo evidence to support our recommendation.