1989/01/01 by Chris D. Orme · 12 citations
Economics, Econometrics and Finance · Mathematics · #Advanced Statistical Methods and Models #Applied mathematics #Econometrics #Economics of Agriculture and Food Markets #Estimator #Mathematical analysis #Mathematics #Maximum likelihood #Regression #Regression analysis #Statistical Methods and Inference #Statistics #Uniqueness
paper · doi:10.1080/07474938908800171
published in Econometric Reviews 8(2), 217-222 (Taylor & Francis)
openalex publication_date 1989/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
In this short note it is demonstrated that although the log-likelihood function for the truncated normal regression model may not be globally concave, it will possess a unique maximum if one exists. This is because the hessian matrix is negative semi-definite when evaluated at any possible solution to the likelihood equations. Since this rules out any saddle points or local minima, more than two local maxima occuring is impossible.