nep-ecm New Economics Papers
on Econometrics
Issue of 2006‒05‒06
nine papers chosen by
Sune Karlsson
Orebro University

  1. Estimating multi-country VAR models By Fabio Canova; Matteo Ciccarelli
  2. An Unobserved Components Model to forecast Austrian GDP By Gerhard Fenz; Martin Spitzer
  4. A SimpleModification to Improve the Finite Sample Properties of Ng and Perron’s Unit Root Tests By Pierre Perron; Zhongjun Qu
  5. New Eurocoin: Tracking Economic Growth in Real Time By Altissimo, Filippo; Cristadoro, Riccardo; Forni, Mario; Lippi, Marco; Veronese, Giovanni
  7. Auto-Dependence Structure of Arch-Models: Tail Dependence Coefficients By Raymond Brummelhuis
  8. Selecting Copulas for Risk Management By Koedijk, Kees; Kole, Erik; Verbeek, Marno
  9. Volatility Clustering, Leverage Effects, and Jump Dynamics in the US and Emerging Asian Equity Markets By Daal, Elton; Naka, Atsuyuki; Yu, Jung-Suk

  1. 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
  2. By: Gerhard Fenz (Oesterreichische Nationalbank, Economic Analysis Division); Martin Spitzer (Oesterreichische Nationalbank, Economic Analysis Division)
    Abstract: This paper deals with forecasting quarterly Austrian GDP growth using monthly conjunctural indicators and state space models. The latter provide an efficient econometric framework to analyse jointly data with different frequencies. Based on a Kalman filter technique we estimate a monthly GDP growth series as an unobserved component using monthly conjunctural indicators as explanatory variables. From a large data set of more than 150 monthly indicators the following six explanatory variables were selected on the basis of their in-sample fit and out of sample forecast performance: the ifo-index, credit growth, vacancies, the real exchange rate, the number of employees and new car registrations. Subsequently, quarterly GDP figures are derived from the monthly unobserved component using a weighted aggregation scheme. Several tests for forecasting accuracy and forecasting encompassing indicate that the unobserved components model (UOC-model) is able to outperform simple ARIMA and Naïve models.
    Date: 2006–03–24
  3. By: Aurea Grane; Josep Fortiana
    Abstract: In a previous paper (Grané and Fortiana 2006) we studied a flexible class of goodness-of-fit tests associated with an orthogonal sequence, the Karhunen-Loève decomposition of a stochastic process derived from the null hypothesis. Generally speaking, these tests outperform Kolmogorov-Smirnov and Cramér-von Mises, but we registered several exceptions. In this work we investigate the cause of these anomalies and, more precisely, whether and when such poor behaviour may be attributed to the orthogonal sequence itself, by replacing it with the Legendre polynomials, a commonly used basis for smooth tests. We find an easily computable formula for the Bahadur asymptotic relative efficiency, a helpful quantity in choosing an adequate basis.
    Date: 2006–04
  4. 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
  5. By: Altissimo, Filippo; Cristadoro, Riccardo; Forni, Mario; Lippi, Marco; Veronese, Giovanni
    Abstract: This paper presents ideas and methods underlying the construction of a timely coincident index that tracks euro-area GDP growth, but, unlike GDP growth, (i) is updated monthly and almost in real time; (ii) is free from seasonal and shorter-run dynamics. We take as target the medium- long-run component of the GDP growth, defined in the frequency domain as including only waves of period larger than one year. We estimate the target by projecting it on generalized principal components extracted from a large panel of monthly macroeconomic series. The main contribution of the paper is that current values of our principal components, derived from a dynamic factor model, act as proxies for future values of GDP growth. In this way we improve with respect to the end-of-sample poor estimation which is typical with band-pass filters. Moreover, as it is defined as an estimate of a target which is observable (although with delay), the performance of our index at the end of the sample can be measured.
    Keywords: band-pass filter; coincident index; generalized principal components; large dataset factor models
    JEL: C51 E32 O30
    Date: 2006–04
  6. 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
  7. 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
  8. By: Koedijk, Kees; Kole, Erik; Verbeek, Marno
    Abstract: Copulas offer financial risk managers a powerful tool to model the dependence between the different elements of a portfolio and are preferable to the traditional, correlation-based approach. In this paper we show the importance of selecting an accurate copula for risk management. We extend standard goodness-of-fit tests to copulas. Contrary to existing, indirect tests, these tests can be applied to any copula of any dimension and are based on a direct comparison of a given copula with observed data. For a portfolio consisting of stocks, bonds and real estate, these tests provide clear evidence in favour of the Student’s t copula, and reject both the correlation-based Gaussian copula and the extreme value-based Gumbel copula. In comparison with the Student’s t copula, we find that the Gaussian copula underestimates the probability of joint extreme downward movements, while the Gumbel copula overestimates this risk. Similarly we establish that the Gaussian copula is too optimistic on diversification benefits, while the Gumbel copula is too pessimistic. Moreover, these differences are significant.
    Keywords: copulas; distributional tests; financial dependence; risk management; tail dependence
    JEL: C12 C14 G11
    Date: 2006–04
  9. 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

This nep-ecm issue is ©2006 by Sune Karlsson. 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.
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