Abstract We present a portfolio choice demand model that allows for the nonparametric estimation of investors’ (subjective) expectations and risk preferences. Using comprehensive 401(k)-plan-level data from 2009 through 2019, we explore heterogeneity in asset allocations using our empirical framework. We recover investors’ beliefs about each asset and examine the implications and potential sources of those beliefs. Heterogeneity in expectations across investors accounts for twice as much variation in portfolio holdings as heterogeneity in risk aversion. Belief heterogeneity is partly driven by investors’ characteristics and experiences, reflecting local sources of information such as county-level GDP and employers’ past performance.

We develop a new nonparametric approach for discrete choice and use it to analyze the demand for health insurance in the California Affordable Care Act marketplace. The model allows for endogenous prices and instrumental variables, while avoiding parametric functional form assumptions about the unobserved components of utility. We use the approach to estimate bounds on the effects of changing premiums or subsidies on coverage choices, consumer surplus, and government spending on subsidies. We find that a $10 decrease in monthly premium subsidies would cause a decline of between 1.8% and 6.7% in the proportion of subsidized adults with coverage. The reduction in total annual consumer surplus would be between $62 and $74 million, while the savings in yearly subsidy outlays would be between $207 and $602 million. We estimate the demand impacts of linking subsidies to age, finding that shifting subsidies from older to younger buyers would increase average consumer surplus, with potentially large impacts on enrollment. We also estimate the consumer surplus impact of removing the highly‐subsidized plans in the Silver metal tier, where we find that a nonparametric model is consistent with a wide range of possibilities. We find that comparable mixed logit models tend to yield price sensitivity estimates toward the lower end of the nonparametric bounds, while producing consumer surplus impacts that can be both higher and lower than the nonparametric bounds depending on the specification of random coefficients.