The information matrix test for Gaussian mixtures

• Dante Amengual ^[1] ; Gabriele Fiorentini ^[2] ; Enrique Sentana ^[1]
  1. [1] CEMFI
  2. [2] Università di Firenze
• Localización: Documentos de Trabajo ( CEMFI ), Nº. 1 (CEMFI Working Paper No. 2401 February 2024), 2024
• Idioma: inglés
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• Resumen

  • In incomplete data models the EM principle implies the moments the Information Matrix test assesses are the expectation given the observations of the moments it would assess were the underlying components observed. This principle also leads to interpretable expressions for their asymptotic covariance matrix adjusted for sampling variability in the parameter estimators under correct specification. Monte Carlo simulations for finite Gaussian mixtures indicate that the parametric bootstrap provides reliable finite sample sizes and good power against various misspecification alternatives. We confirm that 3-component Gaussian mixtures accurately describe cross-sectional distributions of per capita income in the 1960-2000 Penn World Tables