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From |
Nick Cox <n.j.cox@durham.ac.uk> |

To |
"'statalist@hsphsun2.harvard.edu'" <statalist@hsphsun2.harvard.edu> |

Subject |
st: RE: Unit roots in non linear regression models |

Date |
Thu, 10 Feb 2011 11:57:16 +0000 |

I don't understand this at all. If your main idea about dynamics is that of exponential decline, your series can hardly be stationary. The two parts of your question appear to be contradictory. Perhaps you mean something more specific, such as stationarity of some error term, but please clarify. Nick n.j.cox@durham.ac.uk Johannes Muck I would like to estimate a nonlinear regression model of the form y_it = a_i*(1 - exp(-b_i*t)) whereby a_i = exp(a1*x1 + a2*x1^2 + a3*x2 + a4*x3) and b_i = b0 + b1*z1 + b2*z2 The economic interpretation of the model is as follows: y_it denotes company i's market share in period t, a_i denotes company i's long-term market share, and b_it represents company i's speed of convergence towards its long-term market share. y_it is observed for 129 companies for 63 periods on average. I tested whether each of the 129 time series exhibits a unit root using the command -by company, sort: kpss y- the test strongly suggests that most of the 129 time series exhibit a unit root. I have two questions: 1) Can standard unit-root tests be applied although I am estimating a nonlinear model? 2) Is there a possible remedy for the non-stationarity of y_it? From my intuition I would say that using first-differencing will be no use in the nonlinear case. * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**st: RE: RE: Unit roots in non linear regression models***From:*Nick Cox <n.j.cox@durham.ac.uk>

**References**:**st: Unit roots in non linear regression models***From:*Johannes Muck <Johannes.Muck@dice.uni-duesseldorf.de>

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