  Timing decisions are common: when to file your taxes, finish a referee
report, or complete a task at work. We ask whether time preferences can be
inferred when \textsl{only} task completion is observed. To answer this
question, we analyze the following model: each period a decision maker faces
the choice whether to complete the task today or to postpone it to later. Cost
and benefits of task completion cannot be directly observed by the analyst, but
the analyst knows that net benefits are drawn independently between periods
from a time-invariant distribution and that the agent has time-separable
utility. Furthermore, we suppose the analyst can observe the agent's exact
stopping probability. We establish that for any agent with quasi-hyperbolic
$\beta,\delta$-preferences and given level of partial naivete $\hat{\beta}$,
the probability of completing the task conditional on not having done it
earlier increases towards the deadline. And conversely, for any given
preference parameters $\beta,\delta$ and (weakly increasing) profile of task
completion probability, there exists a stationary payoff distribution that
rationalizes her behavior as long as the agent is either sophisticated or fully
naive. An immediate corollary being that, without parametric assumptions, it is
impossible to rule out time-consistency even when imposing an a priori
assumption on the permissible long-run discount factor. We also provide an
exact partial identification result when the analyst can, in addition to the
stopping probability, observe the agent's continuation value.
