arXiv subjects

Ali Hortacsu

Publications and source records attributed to Ali Hortacsu.

3 recordsLinked to original sources

arXiv2018

Semiparametric Estimation of a CES Demand System with Observed and Unobserved Product Characteristics

We develop a characteristics based demand estimation framework for the Marshallian demand system obtained by solving a budget-constrained constant elasticity of substitution (CES) utility maximization problem. From our Marshallian CES demand system, we derive the same market share equation of Berry (1994); Berry, Levinsohn, and Pakes (1995)'s characteristics based logit demand system. Our CES demand estimation framework can accommodate zero predicted and observed market shares by conceptually separating the whether-to-buy decision and how-much-to-buy decision. Furthermore, the estimator we suggest allows a tractable semiparametric estimation strategy that is flexible regarding the distribution of unobservable product characteristics. We apply our framework to scanner data on cola sales, where we show estimated demand curves can be upward sloping if zero market shares are not accommodated properly.

stat.AP↗

arXiv2018

A Memo on the Proof-of-Stake Mechanism

We analyze the economic incentives generated by the proof-of-stake mechanism discussed in the Ethereum Casper upgrade proposal. Compared with proof-of-work, proof-of-stake has a different cost structure for attackers. In Budish (2018), three equations characterize the limits of Bitcoin, which has a proof-of-work mechanism. We investigate their counterparts and evaluate the risk of double-spending attack and sabotage attack. We argue that PoS is safer than PoW agaisnt double-spending attack because of the tractability of attackers, which implies a large "stock" cost for the attacker. Compared to a PoW system whose mining equipments are repurposable, PoS is also safer against a sabotage attack.

cs.CR↗

arXiv2018

Finding Exogenous Variation in Data

We reconsider the classic problem of recovering exogenous variation from an endogenous regressor. Two-stage least squares recovers exogenous variation through presuming the existence of an instrumental variable. We rely instead on the assumption that the regressor is a mixture of exogenous and endogenous observations--say as the result of temporary natural experiments. With this assumption, we propose an alternative two-stage method based on nonparametrically estimating a mixture model to recover a subset of the exogenous observations. We demonstrate that our method recovers exogenous observations in simulation and can be used to find pricing experiments hidden in grocery store scanner data.

stat.AP↗