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Power Properties of Linearity Tests for Time Series (1996)

Teräsvirta, Timo

Cambridge, Mass. : Berkeley Electronic Press (now: De Gruyter)

Studies in nonlinear dynamics and econometrics 1.1996, 1, art2

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ISSN: 1081-1826

Source: Berkeley Electronic Press Academic Journals

Topics: Mathematics , Economics

Notes: This paper examines the power properties of several linearity tests applied in time-series analysis. The tests are the ones Lee et al.(1993) used in their Monte Carlo study. The main tool used for power comparisons in this paper is the Pitman asymptotic relative efficiency. The results generally strengthen the outcome of the simulations and complement some results in Lee et al. (1993). They also suggest guidelines for designing Monte Carlo experiments for linearity tests.

Type of Medium: Electronic Resource

URL: http://www.bepress.com/snde/vol1/iss1/art2

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Modelling nonlinearity in U.S. Gross national product 1889–1987 (1995)

Teräsvirta, Timo

Springer

Empirical economics 20 (1995), S. 577-597

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ISSN: 1435-8921

Keywords: Linearity testing ; nonlinear time series ; smooth transition autoregressive model ; structural change ; univariate time series ; C22 ; E32 ; E37

Source: Springer Online Journal Archives 1860-2000

Topics: Economics

Notes: Abstract This paper considers modelling the annual logarithmed per capita gross national product of the United States in 1889–1987. Some authors have suggested that the parameters of the process generating the data have changed over time but formal parameter constancy tests do not support this argument. The series turns out to be nonlinear and can be adequately characterized by an exponential smooth transition autoregressive model. For comparison, a detrended series is also considered, found nonlinear and modelled using a logistic smooth transition autoregressive model. The behaviour of the estimated models is discussed, and it is seen that nonlinearity is needed to describe the response of the process to exceptionally large exogenous shocks. The properties of the models are further investigated by forecasting several years ahead, and the forecasts are compared with those from other linear and nonlinear models.

Type of Medium: Electronic Resource

URL: http://dx.doi.org/10.1007/BF01206058

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