In parametric models a sufficient condition for local identification is that the vector of moment conditions is differentiable at the true parameter with full rank derivative matrix. We show that there are corresponding sufficient conditions for nonparametric models. A nonparametric rank...

In parametric models a sufficient condition for local identification is that the vector of moment conditions is differentiable at the true parameter with full rank derivative matrix. We show that additional conditions are often needed in nonlinear, nonparametric models to avoid nonlinearities...

The usual t test, the t test based on heteroskedasticity and autocorrelation consistent (HAC) covariance matrix estimators, and the heteroskedasticity and autocorrelation robust (HAR) test are three statistics that are widely used in applied econometric work. The use of these significance tests...

This paper proposes a nonparametric test for common trends in semiparametric panel data models with fixed effects based on a measure of nonparametric goodness-of-fit (R^2). We first estimate the model under the null hypothesis of common trends by the method of profile least squares, and obtain...

Mankind must cooperate to reduce GHG emissions to prevent a catastrophic rise in global temperature. How can the necessary costs of reducing GHG emissions be allocated across regions of the world, within the next few generations, and simultaneously address growth expectations and economic...

We document that the nature of business cycles evolves over the process of development and structural change. In countries with large declining agricultural sectors, aggregate employment is uncorrelated with GDP.During booms, employment in agriculture declines while labor productivity increases...

Climate science indicates that climate stabilization requires low GHG emissions. Is this consistent with nondecreasing human welfare? Our welfare index, called quality of life (QuoL), emphasizes education, knowledge, and the environment. We construct and calibrate a multigenerational model with...

This paper analyzes the properties of standard estimators, tests, and confidence sets (CS's) in a class of models in which the parameters are unidentified or weakly identified in some parts of the parameter space. The paper also introduces methods to make the tests and CS's robust to such...

This paper develops methods of inference for nonparametric and semiparametric parameters defined by conditional moment inequalities and/or equalities. The parameters need not be identified. Confidence sets and tests are introduced. The correct uniform asymptotic size of these procedures is...

This paper determines the properties of standard generalized method of moments (GMM) estimators, tests, and confidence sets (CS's) in moment condition models in which some parameters are unidentified or weakly identified in part of the parameter space. The asymptotic distributions of GMM...