Timing Assumptions and Efficiency: Empirical Evidence in a Production Function Context

Abstract Much of the recent empirical work estimating production functions has used methodologies proposed in two distinct lines of literature: 1) the literature started by Olley and Pakes (1996) on "proxy variable" techniques, and 2) what is commonly referred to as the "dynamic panel" literature. We illustrate how timing and Örm information set assumptions are key to both methodologies, and how these assumptions can be strengthened or weakened almost continously. We also discuss other assumptions that have utilized in these literatures to increase the precision of estimates. Empirically, we then examine how, in a number of plant level production datasets, strengthening or weakening the timing/information set assumptions affects the precision of estimates. We compare these impacts on precision to those achieved by imposing other potential assumptions. This gives the researcher a better idea of the efficiency tradeoffs between different possible assumptions in the production function context.