This paper studies the econometric problems associated with estimation of a stochastic process that is endogenously sampled. Our interest is to infer the law of motion of a discrete-time stochastic process p_t that is observed only at a subset of times t_1, ...,t_n that depend on the outcome of a probabilistic sampling rule that depends on the history of the p_t process as well as other observed covariates x_t. We focus on a particular example where p_t denotes the daily wholesale price of a standardized steel product. There is no centralized spot market for steel, which is better described as a "telephone market" where individual transactions result from private bilateral negotiations between buyers and sellers. Although there is no central record of daily transactions prices in the steel market, we do observe transaction prices for a particular trader --- an intermediary that purchases steel in the wholesale market for subsequent resale in the retail market. The endogenous sampling problem arises from the fact that we only observe p_t on the days that the trader decides to make purchases. We present a parametric analysis of this problem under the assumption that the timing of steel purchases is part of an optimal trading strategy that maximizes the intermediary's expected discounted trading profits. We derive a parametric partial information maximum likelihood (PIML) estimator that solves the endogenous sampling problem and efficiently estimates the unknown parameters of the Markov law of motion for p_t together with the structural parameters that determine the optimal trading rule. We also introduce an alternative consistent, less efficient, but computationally simpler simulated minimum distance (SMD) estimator that avoids high dimensional numerical integrations required by the PIML estimator. Using the SMD estimator, we provide estimates of a truncated lognormal AR(1) model of the wholesale price processes for particular types of steel plate. We use this to infer the fraction of the intermediary's discounted profits that are due to the markups it charges its retail customers, and what fraction is due to pure commodity price speculation, i.e. its success in timing purchases of steel in order to profit from "buying low and selling high."
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