nep-ets New Economics Papers
on Econometric Time Series
Issue of 2006‒05‒06
five papers chosen by
Yong Yin
SUNY at Buffalo

  1. Auto-Dependence Structure of Arch-Models: Tail Dependence Coefficients By Raymond Brummelhuis
  3. Volatility Clustering, Leverage Effects, and Jump Dynamics in the US and Emerging Asian Equity Markets By Daal, Elton; Naka, Atsuyuki; Yu, Jung-Suk
  4. Estimating multi-country VAR models By Fabio Canova; Matteo Ciccarelli
  5. A SimpleModification to Improve the Finite Sample Properties of Ng and Perron’s Unit Root Tests By Pierre Perron; Zhongjun Qu

  1. By: Raymond Brummelhuis (School of Economics, Mathematics & Statistics, Birkbeck College)
    Abstract: We study autodependence in ARCH-models by computing the auto-lower tail dependence coefficients and certain generalizations thereof, for both stationary and non-stationary time series. This study is inspired by financial risk-management issues, and our results are relevant for estimating probabilities of consecutive value-at-risk violations.
    Date: 2006–05
  2. By: Heather M. Anderson; George Athanasopoulos; Farshid Vahid
    Abstract: This paper studies linear and nonlinear autoregressive leading indicator models of business cycles in G7 countries. Our models use the spread between short-term and long-term interest rates as leading indicators for GDP. We examine data admissability by determining whether these models have the ability to produce time series with classical cycles that resemble the observed classical cycles in the data, and then we ask if this data admissability lends itself to better predictions of the probability of recession.
    JEL: C22 C23 E17 E37
    Date: 2006–04
  3. By: Daal, Elton (University of New Orleans); Naka, Atsuyuki (University of New Orleans); Yu, Jung-Suk (University of New Orleans)
    Abstract: This paper proposes asymmetric GARCH-Jump models that synthesize autoregressive jump intensities and volatility feedback in the jump component. Our results indicate that these models provide a better fit for the dynamics of the equity returns in the US and emerging Asian markets, irrespective whether the volatility feedback is generated through a common GARCH multiplier or a separate measure of volatility in the jump intensity function. We also find that they can capture several distinguishing features of the return dynamics in emerging markets, such as, more volatility persistence, less leverage effects, fatter tails, and greater contribution and variability of the jump component.
    Keywords: Volatility feedback, Time-varying jump intensity, Volatility clustering, Leverage effect, Leptokurtosis
    JEL: C22 F31 G15
    Date: 2006–01–20
  4. By: Fabio Canova (Universitat Pompeu Fabra, Department of Economics and Business, Jaume I building, Ramon Trias Fargas, 25-27, 08005-Barcelona, Spain.); Matteo Ciccarelli (European Central Bank, Kaiserstrasse 29, 60311 Frankfurt am Main, Germany.)
    Abstract: This paper describes a methodology to estimate the coefficients, to test specification hypotheses and to conduct policy exercises in multi-country VAR models with cross unit interdependencies, unit specific dynamics and time variations in the coefficients. The framework of analysis is Bayesian: a prior flexibly reduces the dimensionality of the model and puts structure on the time variations; MCMC methods are used to obtain posterior distributions; and marginal likelihoods to check the fit of various specifications. Impulse responses and conditional forecasts are obtained with the output of MCMC routine. The transmission of certain shocks across G7 countries is analyzed.
    Keywords: Multi country VAR, Markov Chain Monte Carlo methods, Flexible priors, Internationalv transmission.
    JEL: C3 C5 E5
    Date: 2006–04
  5. By: Pierre Perron (Department of Economics, Boston University); Zhongjun Qu (University of Illinois at Urbana-Champaign)
    Abstract: The tests introduced by Ng and Perron (2001, Econometrica) have the drawback that for non-local alternatives the power can be very small. The aim of this note is to point out an easy solution to this power reversal problem, which in addition leads to tests having an exact size even closer to nominal size. It involves using OLS instead of GLS detrended data when constructing the modified information criterion.
    Date: 2006–02

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