A GMM estimator using conditional independence of input demands identifies production functions nonparametrically from a single cross-section and produces lower markups and smaller estimated productivity losses than Markov-based methods in Japanese manufacturing data.
6.g jt(Θ)is continuously differentiable inΘin a neighborhood ofΘ 0, and the expected Jacobian matrixG≡E[∇ Θ¯gj(Θ0)]has full column rank
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
econ.EM 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Nonparametric Identification and Estimation of Production Functions Invariant to Productivity Dynamics
A GMM estimator using conditional independence of input demands identifies production functions nonparametrically from a single cross-section and produces lower markups and smaller estimated productivity losses than Markov-based methods in Japanese manufacturing data.