Abstract

https://ssrn.com/abstract=2340539
 


 



Generalizing Smooth Transition Autoregressions


Emilio Zanetti Chini


University of Rome II - Department in Economics, Law and Institutions

October 4, 2013

CEIS Working Paper No. 294

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.

Number of Pages in PDF File: 56

Keywords: Dynamic Asymmetry, Smooth Transition, Testing, Estimation,

JEL Classification: C22, C51, C52


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Date posted: October 16, 2013 ; Last revised: September 27, 2014

Suggested Citation

Zanetti Chini, Emilio, Generalizing Smooth Transition Autoregressions (October 4, 2013). CEIS Working Paper No. 294. Available at SSRN: https://ssrn.com/abstract=2340539 or http://dx.doi.org/10.2139/ssrn.2340539

Contact Information

Emilio Zanetti Chini (Contact Author)
University of Rome II - Department in Economics, Law and Institutions ( email )
Via di Tor Vergata
Rome, Lazio 00133
Italy
(+39) 06 72595715 (Phone)
(+39) 06 2020500 (Fax)
HOME PAGE: http://www.economia.uniroma2.it/mie/sarea.asp?area=9&sarea=52
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