Generalizing smooth transition autoregressions

Emilio Zanetti Chini ()

No 294, CEIS Research Paper from Tor Vergata University, CEIS

Abstract: We introduce a variant of the smooth transition autoregression - the GSTAR model - capable to parametrize the asymmetry in the tails of the transition equation by using a particular generalization of the logistic function. A General-to-Specific modelling strategy is discussed in detail, with particular emphasis on two different LM-type tests for the null of symmetric adjustment towards a new regime and three diagnostic tests, whose power properties are explored via Monte Carlo experiments. Four classical real datasets illustrate the empirical properties of the GSTAR, jointly to a rolling forecasting experiment to evaluate its point and density forecasting performances. In all the cases, the dynamic asymmetry in the cycle is efficiently captured by the new model. The GSTAR beats AR and STAR competitors in point forecasting, while this superiority becomes less evident in density forecasting, specially if robust measures are considered.

Keywords: Dynamic Asymmetry; Smooth Transition; Testing; Estimation; (Density) Forecasting Performance (search for similar items in EconPapers)
JEL-codes: C22 C51 C52 (search for similar items in EconPapers)
Pages: 53 pages
Date: 2013-10-15, Revised 2014-09-25
New Economics Papers: this item is included in nep-ecm and nep-ets
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Related works:
Working Paper: Generalizing Smooth Transition Autoregressions (2017)
Working Paper: Generalizing smooth transition autoregressions (2016)
Working Paper: Generalizing smooth transition autoregressions (2013)
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