Frame the simultaneity as an endogeneity problem and propose an instrumental variables strategy using a valid instrument that shifts price but not ETA directly. Walk through a 2SLS specification with fixed effects and clustered standard errors, then discuss how to compute elasticities and run diagnostic tests. Finally, connect the estimated causal effect to surge pricing decisions, emphasizing practical implications.
Pro tip: Emphasize that the instrument must be exogenous and relevant, and that you would validate it with first-stage F-statistics and overidentification tests if possible. Also, mention that in practice, Uber often uses natural experiments or randomized pricing tests to complement IV estimates.
Clearly state the simultaneity between price and ETA: price affects demand and supply, which in turn affect ETA, and ETA affects price through surge algorithms. Explain why OLS is biased.
Suggest a valid instrument that affects price but not ETA directly, such as cost shifters (e.g., fuel prices, driver incentives) or exogenous demand shocks (e.g., weather, events) that are uncorrelated with ETA conditional on controls.
Outline the two equations: first stage regresses price on the instrument and controls; second stage regresses ETA on instrumented price. Include fixed effects (e.g., time, location) and cluster standard errors at the appropriate level (e.g., city or driver).
Explain why the instrument satisfies the exclusion restriction (only affects ETA through price) and list diagnostic tests: first-stage F-statistic for weak instruments, overidentification test if multiple instruments, and possibly a test for endogeneity (Durbin-Wu-Hausman).
Show how to compute the elasticity of ETA with respect to price from the 2SLS coefficient, and discuss how this elasticity informs optimal surge multipliers to balance supply and demand, considering trade-offs between wait times and affordability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.