Estimating Nonlinear Models

David R. Heise
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David R. Heise: Indiana University

Sociological Methods & Research, 1986, vol. 14, issue 4, 447-472

Abstract: Measurement errors in predictor variables bias multiple regression coefficients, and the problem is particularly complex with nonlinear models containing product variates. This article presents formulas and computing algorithms for correcting data before regression analyses in order to eliminate biases due to measurement errors in models with second- and third-order product variates (e.g., YZ and XYZ). The method requires independent estimates of error variances and covariances for the first-order variates (e.g., X, Y, and Z). An analysis of empirical data illustrates the approach. Errors in estimates are examined through Monte Carlo analyses.

Date: 1986
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Persistent link: https://EconPapers.repec.org/RePEc:sae:somere:v:14:y:1986:i:4:p:447-472

DOI: 10.1177/0049124186014004005

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