nep-ets New Economics Papers
on Econometric Time Series
Issue of 2015‒03‒27
three papers chosen by
Yong Yin
SUNY at Buffalo

  1. Qml inference for volatility models with covariates By Francq, Christian; Thieu, Le Quyen
  2. Structural Vector Autoregressions with Heteroskedasticy By Helmut Lütkepohl; Aleksei Netšunajev; ;
  3. Stochastic Model Specification Search for Time-Varying Parameter VARs By Eric Eisenstat; Joshua C.C. Chan; Rodney Strachan

  1. By: Francq, Christian; Thieu, Le Quyen
    Abstract: The asymptotic distribution of the Gaussian quasi-maximum likelihood estimator (QMLE) is obtained for a wide class of asymmetric GARCH models with exogenous covariates. The true value of the parameter is not restricted to belong to the interior of the parameter space, which allows us to derive tests for the significance of the parameters. In particular, the relevance of the exogenous variables can be assessed. The results are obtained without assuming that the innovations are independent, which allows conditioning on different information sets. Monte Carlo experiments and applications to financial series illustrate the asymptotic results. In particular, an empirical study demonstrates that the realized volatility is an helpful covariate for predicting squared returns, but does not constitute an ideal proxy of the volatility.
    Keywords: APARCH model augmented with explanatory variables; Boundary of the parameter space; Consistency and asymptotic distribution of the Gaussian quasi-maximum likelihood estimator; GARCH-X models; Power-transformed and Threshold GARCH with exogenous covariates
    JEL: C12 C13 C22
    Date: 2015–03
  2. By: Helmut Lütkepohl; Aleksei Netšunajev; ;
    Abstract: A growing literature uses changes in residual volatility for identifying structural shocks in vector autoregressive (VAR) analysis. A number of different models for heteroskedasticity or conditional heteroskedasticity are proposed and used in applications in this context. This study reviews the different volatility models and points out their advantages and drawbacks. It thereby enables researchers wishing to use identification of structural VAR models via heteroskedasticity to make a more informed choice of a suitable model for a specific empirical analysis. An application investigating the interaction between U.S. monetary policy and the stock market is used to illustrate the related issues.
    Keywords: Structural vector autoregression, identication via heteroskedasticity, conditional heteroskedasticity, smooth transition, Markov switching, GARCH
    JEL: C32
    Date: 2015–03
  3. By: Eric Eisenstat (Faculty of Business Administration, University of Bucharest, Romania; RIMIR); Joshua C.C. Chan (Research School of Economics, and Centre for Applied Macroeconomic Analysis, Australian National University); Rodney Strachan (School of Economics, and Centre for Applied Macroeconomic Analysis, University of Queensland; The Rimini Centre for Economic Analysis, Italy)
    Abstract: This article develops a new econometric methodology for performing stochastic model specification search (SMSS) in the vast model space of time-varying parameter VARs with stochastic volatility and correlated state transitions. This is motivated by the concern of over-fitting and the typically imprecise inference in these highly parameterized models. For each VAR coefficient, this new method automatically decides whether it is constant or time-varying. Moreover, it can be used to shrink an otherwise unrestricted time-varying parameter VAR to a stationary VAR, thus providing an easy way to (probabilistically) impose stationarity in time-varying parameter models. We demonstrate the effectiveness of the approach with a topical application, where we investigate the dynamic effects of structural shocks in government spending on U.S. taxes and GDP during a period of very low interest rates.
    Date: 2014–12

This nep-ets issue is ©2015 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.