03993cam a22002657 4500001000600000003000500006005001700011008004100028100001800069245017300087260006600260490005100326500001700377520269800394530006103092538007203153538003603225690011003261690013903371700001603510710004203526830008603568856003703654856003603691t0278NBER20180220051133.0180220s2002 mau||||fs|||| 000 0 eng d1 aHall, George.10aEconometric Methods for Endogenously Sampled Time Seriesh[electronic resource]:bThe Case of Commodity Price Speculation in the Steel Market /cGeorge Hall, John Rust. aCambridge, Mass.bNational Bureau of Economic Researchc2002.1 aNBER technical working paper seriesvno. t0278 aAugust 2002.3 aThis 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 {pt} that is observed only at a subset of times {t1,..., tn} that depend on the outcome of a probabilistic sampling rule that depends on the history of the process as well as other observed covariates xt . We focus on a particular example where pt denotes the daily wholesale price of a standardized steel product. However there are no formal exchanges or centralized markets where steel is traded and pt can be observed. Instead nearly all steel transaction prices are a result of private bilateral negotiations between buyers and sellers, typically intermediated by middlemen known as steel service centers. Even though there is no central record of daily transactions prices in the steel market, we do observe transaction prices for a particular firm -- a steel service center that purchases large quantities of steel in the wholesale market for subsequent resale in the retail market. The endogenous sampling problem arises from the fact that the firm only records pt on the days that it purchases steel. 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 firm'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 a Markov transition probability that determines the law of motion for the underlying {pt} process. The PIML estimator also yields estimates of 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 share of the middleman's discounted profits that are due to markups paid by its retail customers, and the share due to price speculation. The latter measures the firm's success in forecasting steel prices and in timing its purchases in order to buy low and sell high'. The more successful the firm is in speculation (i.e. in strategically timing its purchases), the more serious are the potential biases that would result from failing to account for the endogeneity of the sampling process. aHardcopy version available to institutional subscribers. aSystem requirements: Adobe [Acrobat] Reader required for PDF files. aMode of access: World Wide Web. 7aC1 - Econometric and Statistical Methods and Methodology: General2Journal of Economic Literature class. 7aC6 - Mathematical Methods • Programming Models • Mathematical and Simulation Modeling2Journal of Economic Literature class.1 aRust, John.2 aNational Bureau of Economic Research. 0aTechnical Working Paper Series (National Bureau of Economic Research)vno. t0278.4 uhttp://www.nber.org/papers/t027841uhttp://dx.doi.org/10.3386/t0278