We study the problem of parameter inference in (possibly non-linear and non-smooth) econometric models when the data are measured with error. We allow for arbitrary correlation between the true variables and the measurement errors. To solve the identification problem, we require the existence of an auxiliary data-set that contains information about the conditional distribution of the true variables given the mismeasured variables. Our main assumption requires that the conditional distribution of the true variables given the mismeasured variables is the same in the primary and auxiliary data. Our methods allow the auxiliary data to be a validation sample, where the primary and validation data are from the same distribution, and more importantly, a stratified sample where the auxiliary data-set is not from the same distribution as the primary data. We also show how to combine the two data-sets to obtain a more efficient estimator of the parameter of interest. We establish the large sample properties of the sieve based estimators under verifiable conditions. In particular, we allow for the mismeasured variables to have unbounded supports without employing the tedious trimming scheme typically used in kernel based methods. We illustrate our methods by estimating a returns to schooling censored quantile regression using the CPS/SSR 1978 exact match files where the dependent variable is measured with error of arbitrary kind.

We develop a new estimation methodology for dynamic optimization models with unobserved shocks and deterministic accumulation of the observed state variables. Investment models are an important example of such models. Our pairwise-difference approach exploits two common features of these models: (1) the monotonicity of the agent's decision (policy) function in the shocks, conditional on the observed state variables; and (2) the state-contingent nature of optimal decision making which implies that, conditional on the observed state variables, the variation in observed choices across agents must be due to randomness in the shocks across agents. We illustrate our procedure by estimating a dynamic trading model for the milk production quota market in Ontario, Canada.

We assess empirically the effects of the winner's curse which, in common-value auctions, counsels more conservative bidding as the number of competitors increases. First, we construct an econometric model of an auction in which bidders' preferences have both common- and private-value components, and propose a new monotone quantile approach which facilitates estimation of this model. Second, we estimate the model using bids from procurement auctions held by the State of New Jersey. For a large subset of these auctions, we find that median procurement costs rise as competition intensifies. In this setting, then, asymmetric information overturns the common economic wisdom that more competition is always desirable.