Generalizing smooth transition autoregressions

Emilio Zanetti Chini ()

No 114, DEM Working Papers Series from University of Pavia, Department of Economics and Management

Abstract: We introduce a new time series model capable to parametrize the joint asymmetry in duration and length of cycles - the dynamic asymmetry - by using a particular generalization of the logistic function. The modelling strategy is discussed in detail, with particular emphasis on two different tests for the null of symmetric adjustment and three diagnostic tests, whose power properties are explored via Monte Carlo experiments. Four case studies in classical economic and biological real datasets illustrate the versatility of the new model in different fields. In all the cases, the dynamic asymmetry in the cycle is efficiently detected and modelled. Finally, a rolling forecasting exercise is applied to the resulting estimates. Our model beats linear and conventional nonlinear competitors in point forecasting, while this superiority becomes less evident in density forecasting, specially when relying on robust measures.

Keywords: Dynamic asymmetry; Nonlinear time series; Econometric Modelling; Point forecasts; Density forecasts; Evaluating forecasts; Combining forecasts; Error measures. (search for similar items in EconPapers)
JEL-codes: C22 C51 C52 (search for similar items in EconPapers)
Pages: 54 pages
Date: 2016-01
New Economics Papers: this item is included in nep-ets, nep-for and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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http://dem-web.unipv.it/web/docs/dipeco/quad/ps/RePEc/pav/demwpp/DEMWP0114.pdf (application/pdf)

Related works:
Working Paper: Generalizing Smooth Transition Autoregressions (2017)
Working Paper: Generalizing smooth transition autoregressions (2014)
Working Paper: Generalizing smooth transition autoregressions (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:pav:demwpp:demwp0114

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