White, Halbert; Domowitz, Ian Nonlinear regression with dependent observations. (English) Zbl 0533.62055 Econometrica 52, 143-161 (1984). The authors establish general conditions for consistency and asymptotic normality for the nonlinear least squares estimators. These results are based on the extensions of the law of large numbers and the central limit theorem for random processes with mixing conditions. New tests for model misspecification based on the information matrix testing principle are also given. Reviewer: A.Novikov Cited in 87 Documents MSC: 62J02 General nonlinear regression 62P20 Applications of statistics to economics 60G10 Stationary stochastic processes Keywords:dependent observations; new covariance matrix estimator; new tests for model misspecification; consistency; asymptotic normality; nonlinear least squares estimators; extensions of the law of large numbers; central limit theorem; random processes with mixing conditions; information matrix testing principle PDFBibTeX XMLCite \textit{H. White} and \textit{I. Domowitz}, Econometrica 52, 143--161 (1984; Zbl 0533.62055) Full Text: DOI Link