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Endogeneity in Nonlinear Regressions with Integrated Time Series

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  • Joon Y. Park
  • Yoosoon Chang

Abstract

This paper considers the nonlinear regression with integrated regressors that are contemporaneously correlated with the regression error. We, in particular, establish the consistency and derive the limiting distribution of the nonlinear least squares estimator under such endogeneity for the regressions with the integrable or asymptotically homogeneous regression function. For the regressions with both the integrable and asymptotically homogeneous regression functions, it is shown that the estimator is consistent and has the same rate of convergence as for the case of the regressions with no endogeneity. Whether or not the limiting distribution is affected by the presence of endogeneity, however, depends upon the type of the regression function. If the regression function is asymptotically homogeneous, the limiting distribution of the least squares estimator has an additional term reflecting the presence of endogeneity. On the other hand, the endogeneity does not have any effect on the least squares limit theory, if the regressions function is integrable. Regardless of the presence of endogeneity, the least squares estimator has the same limiting distribution in this case. As an illustration of our theory, we consider the logistic regression with an integrated time series that has contemporaneous correlation with the regression error

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Bibliographic Info

Paper provided by Econometric Society in its series Econometric Society 2004 North American Winter Meetings with number 594.

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Date of creation: 11 Aug 2004
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Handle: RePEc:ecm:nawm04:594

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Related research

Keywords: nonlinear regression; endogeneity; integrated time series; consistency; limit distributions;

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Cited by:
  1. Park Joon Y. & Whang Yoon-Jae, 2005. "A Test of the Martingale Hypothesis," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(2), pages 1-32, June.
  2. Ioannis Kasparis & Peter C.B. Phillips & Tassos Magdalinos, 2012. "Non-linearity Induced Weak Instrumentation," Cowles Foundation Discussion Papers 1872, Cowles Foundation for Research in Economics, Yale University.
  3. Joon Y. Park & J. Isaac Miller, 2004. "Nonlinearity, Nonstationarity, and Thick Tails: How They Interact to Generate Persistency in Memory," Econometric Society 2004 North American Summer Meetings 597, Econometric Society.
  4. Miller, J. Isaac & Park, Joon Y., 2005. "How They Interact to Generate Persistency in Memory," Working Papers 2005-01, Rice University, Department of Economics.

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