Correcting for Omitted-Variable and Measurement-Error Bias in Autoregressive Model Estimation with Panel Data

The parameter estimates based on an econometric equation are biased and can also be inconsistent when relevant regressors are omitted from the equation or when included regressors are measured with error. This problem gets complicated when the `true' functional form of the equation is unknown....

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Bibliographic Details
Published in:Computational economics 2003-10, Vol.22 (2), p.225-253
Main Authors: Mehta, Jatinder, Tavlas, George S, Chang, I-Lok, Swamy, P
Format: Article
Language:English
Subjects:
autoregressive models
Bias
concomitants
Econometrics
Economic conditions
Employment
Estimates
Federal employees
measurement-error biases
omitted-variable biases
panel data
Studies
Unemployment
Variables
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Summary:The parameter estimates based on an econometric equation are biased and can also be inconsistent when relevant regressors are omitted from the equation or when included regressors are measured with error. This problem gets complicated when the `true' functional form of the equation is unknown. Here, we demonstrate how auxiliary variables, called concomitants, can be used to remove omitted-variable and measurement-error biases from the coefficients of an equation with the unknown `true' functional form. The method is specifically designed for panel data. Numerical algorithms for enacting this procedure are presented and an illustration is given using a practical example of forecasting small-area employment from nonlinear autoregressive models. Copyright Kluwer Academic Publishers 2003
ISSN:0927-7099
1572-9974
DOI:10.1023/A:1026189916020