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Non-Linear Regression for Multiple Time-Series

P. M. Robinson
Journal of Applied Probability
Vol. 9, No. 4 (Dec., 1972), pp. 758-768
DOI: 10.2307/3212613
Stable URL: http://www.jstor.org/stable/3212613
Page Count: 11
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Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Non-Linear Regression for Multiple Time-Series
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Abstract

A general multivariate non-linear regression model is considered, including as special cases linear regression when the regression matrix is of less than full rank, simultaneous equations systems and regression on an unobservable predetermined variable. Given a time-series of observations at unit intervals we consider the estimation of the parameters, subject to non-linear constraints, by minimizing a criterion based on the Fourier-transformed model. We allow the residuals to be generated by a stationary, linear, process and establish asymptotic properties of our estimates.

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