Calculating Standard Errors of Predicted Values Based on Nonlinear Functional Forms
Gary King. 1991.
"Calculating Standard Errors of Predicted Values Based on Nonlinear Functional Forms".
The Political Methodologist, 4.

Abstract
Whenever we report predicted values, we should also report some measure of the uncertainty of these estimates. In the linear case, this is relatively simple, and the answer well-known, but with nonlinear models the answer may not be apparent. This short article shows how to make these calculations. I first present this for the familiar linear case, also reviewing the two forms of uncertainty in these estimates, and then show how to calculate these for any arbitrary function. An example appears last.
See Also
- [Software] CLARIFY: Software for Interpreting and Presenting Statistical Results (2003)
- [Paper] Google Flu Trends Still Appears Sick: An Evaluation of the 2013‐2014 Flu Season (2014)
- [Paper] How Robust Standard Errors Expose Methodological Problems They Do Not Fix, and What to Do About It (2015)
- [Paper] Making the Most of Statistical Analyses: Improving Interpretation and Presentation (2000)
- [Paper] The Parable of Google Flu: Traps in Big Data Analysis (2014)
- [Paper] Toward A Common Framework for Statistical Analysis and Development (2008)
- [Paper] Twitter: Big Data Opportunities—Response (2014)
- [Book] Unifying Political Methodology: The Likelihood Theory of Statistical Inference (1998)