Threshold quantile autoregressive models
GALVAO, ANTONIO; MONTES ROJAS, GABRIEL VICTORIO; OLMO, JOSE
JOURNAL OF TIME SERIES ANALYSIS
WILEY-BLACKWELL PUBLISHING, INC
This article studies estimation and asymptotic properties of Threshold Quantile Autoregressive processes. In particular, we show the consistency of the threshold and slope parameter estimators for each quantile and regime, and derive the asymptotic normality of the slope parameter estimators. A Monte Carlo experiment shows that the standard ordinary least squares estimation method is not able to detect important nonlinearities produced in the
quantile process. The empirical study concentrates on modelling the dynamics of the conditional distribution of unemployment growth after the second world war. The results show evidence of important heterogeneity associated with unemployment and strong asymmetric persistence of unemployment growth in the higher quantiles.