A Robbins–Monro Algorithm for Non‐Parametric Estimation of NAR Process with Markov Switching: Consistency

Fermin, LisandroLisandroFerminRicardo RiosLuis Angel Rodriguez2025-04-142025-04-142017-04-2010.1111/jtsa.12237https://cris-uv.scimago.es/handle/123456789/2375WOS:000413152100002We approach the problem of non‐parametric estimation for autoregressive Markov switching processes. In this context, the Nadaraya–Watson‐type regression functions estimator is interpreted as a solution of a local weighted least‐square problem, which does not admit a closed‐form solution in the case of hidden Markov switching. We introduce a non‐parametric recursive algorithm to approximate the estimator. Our algorithm restores the missing data by means of a Monte Carlo step and estimates the regression function via a Robbins–Monro step.acceso abiertoSTATISTICS PROBABILITYA Robbins–Monro Algorithm for Non‐Parametric Estimation of NAR Process with Markov Switching: Consistencyjournal-article