Correcting for Omitted-Variable and Measurement-Error Bias in Autoregressive Model Estimation with Panel Data |
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| Authors: | P AV B Swamy I-Lok Chang Jatinder S Mehta George S Tavlas |
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| Affiliation: | (1) Statistical Methods Division, Office of Employment and Unemployment Statistics, Bureau of Labor Statistics, Washington, DC, 20212, U.S.A;(2) Department of Mathematics and Statistics, The American University, Washington, DC, 20016, U.S.A;(3) Department of Mathematics, Temple University, Philadelphia, PA, 19122, U.S.A;(4) Economics Research Department, Bank of Greece, Athens, Greece |
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| Abstract: | 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. |
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| Keywords: | autoregressive models omitted-variable biases measurement-error biases concomitants panel data |
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