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

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Author Info

  • P. Swamy
  • I-Lok Chang
  • Jatinder Mehta
  • George Tavlas

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. Copyright Kluwer Academic Publishers 2003

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File URL: http://hdl.handle.net/10.1023/A:1026189916020
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Bibliographic Info

Article provided by Society for Computational Economics in its journal Computational Economics.

Volume (Year): 22 (2003)
Issue (Month): 2 (October)
Pages: 225-253

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Handle: RePEc:kap:compec:v:22:y:2003:i:2:p:225-253

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Web page: http://www.springerlink.com/link.asp?id=100248
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Related research

Keywords: autoregressive models; omitted-variable biases; measurement-error biases; concomitants; panel data;

References

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  1. Basmann, R. L., 1988. "Causality tests and observationally equivalent representations of econometric models," Journal of Econometrics, Elsevier, vol. 39(1-2), pages 69-104.
  2. Friedman, Milton & Schwartz, Anna J, 1991. "Alternative Approaches to Analyzing Economic Data," American Economic Review, American Economic Association, vol. 81(1), pages 39-49, March.
  3. Hans M. Amman & David A. Kendrick, . "Computational Economics," Online economics textbooks, SUNY-Oswego, Department of Economics, number comp1, January.
  4. P.A.V.B. Swamy & Garry J. Schinasi, 1986. "Should fixed coefficients be reestimated every period for extrapolation?," International Finance Discussion Papers 287, Board of Governors of the Federal Reserve System (U.S.).
  5. I-Lok Chang & P.A.V.B. Swamy & Charles Hallahan & George S. Tavlas, 2000. "A Computational Approach to Finding Causal Economic Laws," Computational Economics, Society for Computational Economics, vol. 16(1/2), pages 105-136, October.
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Cited by:
  1. Swamy, P.A.V.B. & Yaghi, Wisam & Mehta, Jatinder S. & Chang, I-Lok, 2007. "Empirical best linear unbiased prediction in misspecified and improved panel data models with an application to gasoline demand," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3381-3392, April.
  2. Swamy, P.A.V.B. & Mehta, Jatinder S. & Chang, I-Lok & Zimmerman, T.S., 2009. "An efficient method of estimating the true value of a population characteristic from its discrepant estimates," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2378-2389, April.
  3. Jonathan E. Leightner, 2013. "The Changing Effectiveness of Monetary Policy," Economies, MDPI, Open Access Journal, vol. 1(3), pages 49-64, November.
  4. George Hondroyiannis & P.A.V.B. Swamy & George S. Tavlas & Michael Ulan, 2005. "Some Further Evidence on Exchange-Rate Volatility and Exports," Working Papers 28, Bank of Greece.
  5. Hall, Stephen G. & Hondroyiannis, George & Swamy, P.A.V.B. & Tavlas, George S., 2009. "Assessing the causal relationship between euro-area money and prices in a time-varying environment," Economic Modelling, Elsevier, vol. 26(4), pages 760-766, July.

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