The paper develops a new and easy-to-use F test in a time series GMM framework that allows for general forms of serial dependence. The test is based on the Wald statistic with a multiplicative correction factor and employs critical values from a standard F distribution. The F critical values are high-order correct under the conventional asymptotics. Monte Carlo simulations show that the F test ...

Significant nonlinearities are found in several cyclical components macroeconomic time series across countries. Standard equilibrium models of business cycles successfully explain most first and second moments of these time series. However, this paper shows that a model of this class cannot replicate nonlinear features of the data. Applying the Efficient Method of Moments methodology to build a...

In parametric stochastic frontier models, the composed error is specified as the sum of a two-sided noise component and a one-sided inefficiency component, which is usually assumed to be half-normal, implying that the error distribution is skewed in one direction. In practice, however, estimation residuals may display skewness in the wrong direction. Model respecification or pulling a new sampl...

This paper is concerned with developing uniform confidence bands for functions estimated nonparametrically with instrumental variables. We show that a sieve nonparametric instrumental variables estimator is pointwise asymptotically normally distributed. The asymptotic normality result holds in both mildly and severely ill-posed cases. We present an interpolation method to obtain a uniform confi...

This paper presents a Monte Carlo comparison of several versions of heteroscedasticity robust standard errors (HRSEs) to a nonparametric feasible generalized least squares procedure (NPGLS). Results suggest that the NPGLS procedure provides an improvement in efficiency ranging from 3% to 12% or more in reasonable sample sizes using simple functional forms for heteroscedasticity. This results in...

We introduce a class of instrumental quantile regression methods for heterogeneous treatment effect models and simultaneous equations models with nonadditive errors and offer computable methods for estimation and inference. These methods can be used to evaluate the impact of endogenous variables or treatments on the entire distribution of outcomes. We describe an estimator of the instrumental v...

In this note, we propose a least squares method with l1 penalty (based on the “Lasso”) to estimate models with latent variables. Our approach addresses the high dimensionality of these models, due to the presence of unknown distribution functions. It builds on a recent proposal by Bunea, Tsybakov, Wegkamp and Barbu (2010, Annals of Statistics) that uses penalized least squares for density estim...

We estimate the fraction of disease cases, and the fraction of their total medical expenditures, attributable to smoking for two disease groups: (LC) lung and laryngeal cancer and chronic obstructive pulmonary disease, (CHD) cardiovascular disease, stroke and other smoking-caused cancers. We use a generalized additive model to predict the probability of disease; and a semi-parametric, two-part ...

We introduce a class of instrumental quantile regression methods for heterogeneous treatment effect models and simultaneous equations models with nonadditive errors and offer computable methods for estimation and inference. These methods can be used to evaluate the impact of endogenous variables or treatments on the entire distribution of outcomes. We describe an estimator of the instrumental v...

This paper uses a new non-parametric, unconditional, hyperbolic order-α quantile estimator to construct a hyperbolic version of the Malmquist index. Unlike traditional non-parametric efficiency estimators, the new estimator is both robust to data outliers and has a root-n convergence rate. We use this estimator to examine changes in the efficiency and productivity of U.S. banks between 1985 and...