Minimum Distance Estimation of Dynamic Models with Errors-In-Variables
Nikolay Gospodinov, Ivana Komunjer, and Serena Ng
Working Paper 2014-11
Empirical analysis often involves using inexact measures of desired predictors. The bias created by the correlation between the problematic regressors and the error term motivates the need for instrumental variables estimation. This paper considers a class of estimators that can be used when external instruments may not be available or are weak. The idea is to exploit the relation between the parameters of the model and the least squares biases. In cases when this mapping is not analytically tractable, a special algorithm is designed to simulate the latent predictors without completely specifying the processes that induce the biases. The estimators perform well in simulations of the autoregressive distributed lag model and the dynamic panel model. The methodology is used to re-examine the Phillips curve, in which the real activity gap is latent.
JEL classification: C1, C3
Key words: measurement error, minimum distance, simulation estimation, dynamic panel
The authors acknowledge financial support from National Science Foundation grants SES-0962473 and SES-0962431. The views expressed here are the authors' and not necessarily those of the Federal Reserve Bank of Atlanta or the Federal Reserve System. Any remaining errors are the authors' responsibility.
Please address questions regarding content to Nikolay Gospodinov, Research Department, Federal Reserve Bank of Atlanta, 1000 Peachtree Street NE, Atlanta, GA 30309-4470, 404-498-7892, email@example.com; Ivana Komunjer, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, firstname.lastname@example.org; or Serena Ng (corresponding author), Columbia University, 420 W. 118 Street, MC 3308, New York, NY 10027, email@example.com.
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