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We introduce a new method for conditioning out serially correlated unobserved shocks to the production technology by building ideas first developed in Olley and Pakes (1996). Olley and Pakes show how to use investment to control for correlation between input levels and the unobserved firmspecific productivity process. We prove that like investment, intermediate inputs (those inputs which are typically subtracted out in a value-added production function) can also solve this simultaneity problem. We highlight three potential advantages to using an intermediate inputs approach relative to investment. Our results indicate that these advantages are empirically important.

We add to the methods for conditioning out serially correlated unobserved shocks to the production technology. We build on ideas first developed in Olley and Pakes (1996). They show how to use investment to control for correlation between input levels and the unobserved firm-specific productivity process. We show that intermediate inputs (those inputs which are typically subtracted out in a value-added production function) can also solve this simultaneity problem. We discuss some theoretical benefits of extending the proxy choice set in this direction and our empirical results suggest these benefits can be important.

In this paper, we consider how rich sources of information on consumer choice can help to identify demand parameters in a widely used class of differentiated products demand models. Most important, we show how to use "second-choice" data on automotive purchases to obtain good estimates of substitution patterns in the automobile industry. We use our estimates to make out-of-sample predictions about important recent changes in industry structure.We thank numerous seminar participants, two referees, and the editors Lars Hansen and John Cochrane for helpful suggestions. We also thank the National Science Foundation for financial support, through grants 9122672, 9512106, and 9617887. We are particularly grateful to G.