nep-ets New Economics Papers
on Econometric Time Series
Issue of 2007‒12‒08
seven papers chosen by
Yong Yin
SUNY at Buffalo

  1. A new class of distribution-free tests for time series models specification By Miguel A. Delgado; Carlos Velasco
  2. Forecasting using Bayesian and information theoretic model averaging: an application to UK in flation By George Kapetanios; Vincent Labhard; Simon Price
  3. A residual-based cointegration test for near unit root variables By Erik Hjalmarsson; Par Osterholm
  4. "Estimating Stochastic Volatility Models Using Daily Returns and Realized Volatility Simultaneously" By Makoto Takahashi; Yasuhiro Omori; Toshiaki Watanabe
  5. Hierarchical Markov normal mixture models with applications to financial asset returns By John Geweke; Gianni Amisano
  6. Why Bayes Rules: A Note on Bayesian vs. Classical Inference in Regime Switching Models By Dennis Gaertner
  7. Business Conditions, Stock Market Volatility, and Expected Return By Chang-Jin Kim; Yunmi Kim; Charles R. Nelson

  1. By: Miguel A. Delgado; Carlos Velasco
    Abstract: The construction of asymptotically distribution free time series model specification tests using as statistics the estimated residual autocorrelations is considered from a general view point. We focus our attention on Box-Pierce type tests based on the sum of squares of a few estimated residual autocorrelations. This type of tests belongs to the class defined by quadratic forms of weighted residual autocorrelations, where weights are suitably transformed resulting in asymptotically distribution free tests. The weights can be optimally chosen to maximize the power function when testing in the direction of local alternatives. The optimal test in this class against MA, AR or Bloomfield alternatives is a Box-Pierce type test based on the sum of squares of a few transformed residual autocorrelations. Such transformations are, in fact, the recursive residuals in the projection of the residual autocorrelations on a certain score function.
    Date: 2007–11
  2. By: George Kapetanios (Queen Mary University of London); Vincent Labhard (Bank of England); Simon Price (Department of Economics, City University, London)
    Abstract: Model averaging often improves forecast accuracy over individual forecasts. It may also be seen as a means of forecasting in data-rich environments. Bayesian model averaging methods have been widely advocated, but a neglected frequentist approach is to use information theoretic based weights. We consider the use of information-theoretic model averaging in forecasting UK inflation, with a large data set, and find that it can be a powerful alternative to Bayesian averaging schemes.
    Keywords: forecasting, inflation, Bayesian model averaging, Akaike criteria, forecast combining.
    JEL: C11 C15 C53
    Date: 2007–11
  3. By: Erik Hjalmarsson; Par Osterholm
    Abstract: Methods of inference based on a unit root assumption in the data are typically not robust to even small deviations from this assumption. In this paper, we propose robust procedures for a residual-based test of cointegration when the data are generated by a near unit root process. A Bonferroni method is used to address the uncertainty regarding the exact degree of persistence in the process. We thus provide a method for valid inference in multivariate near unit root processes where standard cointegration tests may be subject to substantial size distortions and standard OLS inference may lead to spurious results. Empirical illustrations are given by: (i) a re-examination of the Fisher hypothesis, and (ii) a test of the validity of the cointegrating relationship between aggregate consumption, asset holdings, and labor income, which has attracted a great deal of attention in the recent finance literature.
    Date: 2007
  4. By: Makoto Takahashi (Graduate School of Economics, University of Tokyo); Yasuhiro Omori (Faculty of Economics, University of Tokyo); Toshiaki Watanabe (Institute of Economic Research, Hitotsubashi University)
    Abstract: Realized volatility, which is the sum of squared intraday returns over a certain interval such as a day, has recently attracted the attention of financial economists and econometricians as an accurate measure of the true volatility. In the real market, however, the presence of non-trading hours and market microstructure noise in transaction prices may cause the bias in the realized volatility. On the other hand, daily returns are less subject to the noise and therefore may provide additional information on the true volatility. From this point of view, we propose modeling realized volatility and daily returns simultaneously based on well-known stochastic volatility model. Using intraday data of Tokyo stock price index, we show that this model can estimate realized volatility biases and parameters simultaneously.We take a Bayesian approach and propose an efficient sampling algorithm to implement the Markov chain Monte Carlo method for our simultaneous model. The result of the model comparison between the simultaneous models using both naive and scaled realized volatilities indicates that the effect of non-trading hours is more essential than that of microstructure noise but still the latter has to be considered for better fitting. Our Bayesian approach has an advantage over the conventional two-step correction procedure in that we are able to take the uncertainty in estimation of both biases and parameters into account for the prediction and the evaluation of Value-at-Risk.
    Date: 2007–09
  5. By: John Geweke (Corresponding author: Department of Economics , University of Iowa, Iowa City IA 52242, USA.); Gianni Amisano (European Central Bank, Kaiserstrasse 29, 60311 Frankfurt am Main, Germany.)
    Abstract: With the aim of constructing predictive distributions for daily returns, we introduce a new Markov normal mixture model in which the components are themselves normal mixtures. We derive the restrictions on the autocovariances and linear representation of integer powers of the time series in terms of the number of components in the mixture and the roots of the Markov process. We use the model prior predictive distribution to study its implications for some interesting functions of returns. We apply the model to construct predictive distributions of daily S&P500 returns, dollar-pound returns, and one- and ten-year bonds. We compare the performance of the model with ARCH and stochastic volatility models using predictive likelihoods. The model's performance is about the same as its competitors for the bond returns, better than its competitors for the S&P 500 returns, and much better for the dollar-pound returns. Validation exercises identify some potential improvements. JEL Classification: C53, G12, C11, C14.
    Keywords: Asset returns, Bayesian, forecasting, MCMC, mixture models.
    Date: 2007–11
  6. By: Dennis Gaertner (Socioeconomic Institute, University of Zurich)
    Abstract: By means of a very simple example, this note illustrates the appeal of using Bayesian rather than classical methods to produce inference on hidden states in models of Markovian regime switching.
    Keywords: Bayesian analysis, switching regression, regime changes, nonlinear filtering
    JEL: C11 C22
    Date: 2007–12
  7. By: Chang-Jin Kim; Yunmi Kim; Charles R. Nelson
    Date: 2007–12

This nep-ets issue is ©2007 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.