We assess the finite sample performance of simulation estimators that are used to estimate the parameters of dynamic corporate finance models. We formulate an external validity specification test and propose a new set of statistical benchmarks that can be used to estimate and evaluate these models. These benchmarks are based on model policy functions. Our Monte Carlo simulations show that the estimators are largely unbiased with low root mean squared errors. When computed with an optimal weight matrix, the specification tests associated with the estimators are close to correctly sized. These tests have excellent power to detect misspecification.

We study the impact of labor market frictions on asset prices. In the cross section of US firms, a 10 percentage point increase in the firm’s hiring rate is associated with a 1.5 percentage point decrease in the firm’s annual risk premium. We propose an investment-based model with stochastic labor adjustment costs to explain this finding. Firms with high hiring rates are expanding firms that incur high adjustment costs. If the economy experiences a shock that lowers adjustment costs, these firms benefit the most. The corresponding increase in firm value operates as a hedge against these shocks, explaining the lower risk premium of these firms in equilibrium.

We assess the finite sample performance of simulation estimators that are used to estimate the parameters of dynamic corporate finance models. We formulate an external validity specification test and propose a new set of statistical benchmarks that can be used to estimate and evaluate these models. These benchmarks are based on model policy functions. Our Monte Carlo simulations show that the estimators are largely unbiased with low root mean squared errors. When computed with an optimal weight matrix, the specification tests associated with the estimators are close to correctly sized. These tests have excellent power to detect misspecification. Received August 19, 2016; editorial decision May 30, 2017 by Editor Stijn Van Nieuwerburgh.