nep-ets New Economics Papers
on Econometric Time Series
Issue of 2017‒08‒06
three papers chosen by
Yong Yin
SUNY at Buffalo

  1. Narrative Sign Restrictions for SVARs By Juan Antolín-Díaz; Juan F. Rubio-Ramírez
  2. Structural change in non-stationary AR(1) models By Chong, Terence Tai Leung; Pang, Tianxiao; Zhang, Danna; Liang, Yanling
  3. The Memory of Volatility By Wenger, Kai; Leschinski, Christian; Sibbertsen, Philipp

  1. By: Juan Antolín-Díaz; Juan F. Rubio-Ramírez
    Abstract: We identify structural vector autoregressions using narrative sign restrictions. Narrative sign restrictions constrain the structural shocks and the historical decomposition around key historical events, ensuring that they agree with the established narrative account of these episodes. Using models of the oil market and monetary policy, we show that narrative sign restrictions are highly informative. We highlight that adding a single narrative sign restriction dramatically sharpens and even changes the inference of SVARs originally identified via traditional sign restrictions. Our approach combines the appeal of narrative methods with the popularized usage of traditional sign restrictions.
    Date: 2017–03
  2. By: Chong, Terence Tai Leung; Pang, Tianxiao; Zhang, Danna; Liang, Yanling
    Abstract: This paper revisits the asymptotic inference for non-stationary AR(1) models of Phillips and Magdalinos (2007a) by incorporating a structural change in the AR parameter at an unknown time k0. We derive the limiting distributions of the t-ratios of beta1 and beta2 and the least squares estimator of the change point for the cases above under some mild conditions. Monte Carlo simulations are conducted to examine the finite-sample properties of the estimators. Our theoretical findings are supported by the Monte Carlo simulations.
    Keywords: AR(1) model, Least squares estimator, Limiting distribution, Mildly explosive, Mildly integrated, Structural change, Unit root.
    JEL: C2 C22
    Date: 2017
  3. By: Wenger, Kai; Leschinski, Christian; Sibbertsen, Philipp
    Abstract: The focus of the volatility literature on forecasting and the predominance of the conceptually simpler HAR model over long memory stochastic volatility models has led to the fact that the actual degree of memory estimates has rarely been considered. Estimates in the literature range roughly between 0.4 and 0.6 - that is from the higher stationary to the lower non-stationary region. This difference, however, has important practical implications - such as the existence or non-existence of the fourth moment of the return distribution. Inference on the memory order is complicated by the presence of measurement error in realized volatility and the potential of spurious long memory. In this paper we provide a comprehensive analysis of the memory in variances of international stock indices and exchange rates. On the one hand, we find that the variance of exchange rates is subject to spurious long memory and the true memory parameter is in the higher stationary range. Stock index variances, on the other hand, are free of low frequency contaminations and the memory is in the lower non-stationary range. These results are obtained using state of the art local Whittle methods that allow consistent estimation in presence of perturbations or low frequency contaminations.
    Keywords: Realized Volatility; Long Memory; Perturbation; Spurious Long Memory
    JEL: C12 C22 C58 G15
    Date: 2017–07

This nep-ets issue is ©2017 by Yong Yin. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.