Events Calendar

Econometrics Workshop, Zhutong Gu, Rutgers Univ. Ph.D. Graduate Student

Econometrics

Thursday, October 06, 2016, 04:00pm - 05:30pm

Zhutong Gu, Rutgers University (Ph.D Graduate Student)

"Testing Additive Separability with Excess Unobserved Heterogeneity: An Investigation of Hicksian-neutral Productivity in U.S. Manufacturing Industry 1990-2001"

Abstract: Additive separability between observables and unobservables is one of the essential properties in structural modeling of heterogeneity in the presence of endogeneity. In this paper, we propose a simple test based on quantile average differences of the average structural functions (ASF) generated by nonparametric nonseparable and separable models with unrestricted heterogeneity. Given identification, we establish conditions under which structural additivity is equivalent to the equality of ASFs derived from the two competing specifications commonly employed. We estimate the reduced form regressions by Nadaraya-Watson (NW) estimators and control the asymptotic bias by an iterative procedure proposed in Klein and Shen (2016). We show that the asymptotic test statistic follows a central chi-sq distribution under the null hypothesis and has power against a sequence of root-N local alternatives. Our proposed test statistic works reasonably well in a series of finite sample simulations with analytic variances, alleviating the computational burden often involved in bootstrapped inferences. We also show that the test can be straightforwardly extended to semiparametric models, panel data and triangular simultaneous equations frameworks. In the empirical application, we consider the context of production function estimation using firm-level data of U.S. manufacturing industry from 1990 to 2011. To control for the simultaneity bias, we generalize the proxy function approach to nonseparable models and then test for Hicksian-neutral technology in the presence of of multi-dimensional productivity shocks.

Location  NJ Hall 3rd Floor Library
Contact  Xiye Yang