RT Generic
T1 Threshold stochastic unit root models
A1 Gonzalo, Jesús
A1 Montesinos, Raquel
A2 Universidad Carlos III de Madrid, 
K1 Dickey-Fuller test
K1 Difference stationary
K1 Nonstationary time series
K1 Stochastic difference equations
K1 Stochastic unit roots
K1 Time varying coefficients
K1 Threshold models
K1 Unit roots
AB This paper introduces a new class of stochastic unit root (STUR) processes, where the randomness of the autorregresive unit root is driven by a threshold variable. These new models, the threshold autorregresive stochastic unit root (TARSUR) models, are stationary in some regimes and mildly explosive in others. TARSUR models are not only an alternative to fixed unit root models but present interpretation, estimation and testing advantages withrespect to the existent STUR models. The paper analyzes the stationarity properties ofthe TARSUR models and proposes a simple t -statistic for testing the null hypothesis of a fixed unit root versus a stochastic unit root hypothesis. It is shown that its asymptotic distribution (AD) depends on the knowledge we have about the threshold values: known, unknown but identified, and unknown and unidentified. In the first two cases the AD is a standard Normal distribution, while in the last one the AD is a functional of Brownian Motions and Brownian Sheets. Monte Carlo simulations show that the proposed tests behave very well in finite samples and that the Dickey-Fuller test cannot easily distinguishbetween an exact unit root and a threshold stochastic unit root. The paper concludes with applications to stock prices and interest rates where the hypothesis of a fixed unit root is rejected in favor of the threshold stochastic unit root.
YR 2002
FD 2002-09
LK https://hdl.handle.net/10016/3240
UL https://hdl.handle.net/10016/3240
LA eng
DS e-Archivo
RD 18 may. 2024