The information matrix test for Markov switching autoregressive models with covariate-dependent transition probabilities

• Dante Amengual ^[1] ; Gabriele Fiorentini ^[2] ; Enrique Sentana ^[1]
  1. [1] CEMFI
  2. [2] Università di Firenze and RCEA
• Localización: Documentos de Trabajo ( CEMFI ), Nº. 2 (CEMFI Working Paper No. 2502 January 2025), 2025
• Idioma: inglés
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• Resumen

  • The EM principle implies the moments underlying the information matrix test for multivariate Markov switching autoregressive models with covariate-dependent transition probabilities are the smoothed values of the moments we would test were the latent Markov chain observed. Thus, we identify components related to the heteroskedasticity, skewness and kurtosis of the multivariate regression residuals for each of the regimes, the neglected multivariate heteroskedasticity of the generalised residuals for each of the columns of the transition matrix, and a final component that assesses the conditional independence of these generalised residuals and the regression residuals, their squares and cross-products given the observed variables.