Ricardo RiosLuis Angel RodriguezFermín, LisandroLisandroFermín2025-04-142025-04-142017-04-2010.1111/jtsa.122372-s2.0-85018651565https://cris-uv-2.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. We prove that non‐parametric autoregressive models with Markov switching are identifiable when the hidden Markov process has a finite state space. Consistency of the estimator is proved using the strong α ‐mixing property of the model. Finally, we present some simulations illustrating the performances of our non‐parametric estimation procedure.enacceso abiertoApplied MathematicsMathematics, Interdisciplinary ApplicationsStatistics And ProbabilityStatistics, Probability And Uncertaintyarticle