  Standard methods for estimating production functions in the Olley and Pakes
(1996) tradition require assumptions on input choices. We introduce a new
method that exploits (increasingly available) data on a firm's expectations of
its future output and inputs that allows us to obtain consistent production
function parameter estimates while relaxing these input demand assumptions. In
contrast to dynamic panel methods, our proposed estimator can be implemented on
very short panels (including a single cross-section), and Monte Carlo
simulations show it outperforms alternative estimators when firms' material
input choices are subject to optimization error. Implementing a range of
production function estimators on UK data, we find our proposed estimator
yields results that are either similar to or more credible than commonly-used
alternatives. These differences are larger in industries where material inputs
appear harder to optimize. We show that TFP implied by our proposed estimator
is more strongly associated with future jobs growth than existing methods,
suggesting that failing to adequately account for input endogeneity may
underestimate the degree of dynamic reallocation in the economy.
