Extended rank likelihood regression unifies continuous and ordinal outcomes under a monotone latent linear model with no asymptotic efficiency loss at the continuous and binary extremes and practical conditional prediction intervals.
A Modern Gauss-Markov Theorem? Really?
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abstract
We show that the theorems in Hansen (2021a) (the version accepted by Econometrica), except for one, are not new as they coincide with classical theorems like the good old Gauss-Markov or Aitken Theorem, respectively; the exceptional theorem is incorrect. Hansen (2021b) corrects this theorem. As a result, all theorems in the latter version coincide with the above mentioned classical theorems. Furthermore, we also show that the theorems in Hansen (2022) (the version published in Econometrica) either coincide with the classical theorems just mentioned, or contain extra assumptions that are alien to the Gauss-Markov or Aitken Theorem.
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Extended rank regression for all ordinal data
Extended rank likelihood regression unifies continuous and ordinal outcomes under a monotone latent linear model with no asymptotic efficiency loss at the continuous and binary extremes and practical conditional prediction intervals.