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A perturbation analysis of Markov chains models with time-varying parameters. (English) Zbl 1455.60092

Summary: We study some regularity properties in locally stationary Markov models which are fundamental for controlling the bias of nonparametric kernel estimators. In particular, we provide an alternative to the standard notion of derivative process developed in the literature and that can be used for studying a wide class of Markov processes. To this end, for some families of \(V\)-geometrically ergodic Markov kernels indexed by a real parameter \(u\), we give conditions under which the invariant probability distribution is differentiable with respect to \(u\), in the sense of signed measures. Our results also complete the existing literature for the perturbation analysis of Markov chains, in particular when exponential moments are not finite. Our conditions are checked on several original examples of locally stationary processes such as integer-valued autoregressive processes, categorical time series or threshold autoregressive processes.

MSC:

60J05 Discrete-time Markov processes on general state spaces
62G05 Nonparametric estimation
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