Model-based INAR bootstrap for forecasting INAR(p) models

Luisa Bisaglia () and Margherita Gerolimetto ()
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Luisa Bisaglia: University of Padova
Margherita Gerolimetto: Ca’ Foscari University Venice

Computational Statistics, 2019, vol. 34, issue 4, No 17, 1815-1848

Abstract: Abstract In this paper we analyse some bootstrap techniques to make inference in INAR(p) models. First of all, via Monte Carlo experiments we compare the performances of these methods when estimating the thinning parameters in INAR(p) models; we state the superiority of model-based INAR bootstrap approaches on block bootstrap in terms of low bias and Mean Square Error. Then we adopt the model-based bootstrap methods to obtain coherent predictions and confidence intervals in order to avoid difficulty in deriving the distributional properties. Finally, we present an empirical application.

Keywords: INAR(p) models; Estimation; Forecast; Bootstrap (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s00180-019-00902-1

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