nep-ets New Economics Papers
on Econometric Time Series
Issue of 2010‒02‒27
four papers chosen by
Yong Yin
SUNY at Buffalo

  1. Multivariate Methods for Monitoring Structural Change By Jan J.J. Groen; George Kapetanios; Simon Price
  2. Bootstrap Sequential Determination of the Co-integration Rank in VAR Models By Giuseppe Cavaliere; Anders Rahbek; A. M. Robert Taylor
  3. Econometric Analysis of Continuous Time Models: A Survey of Peter Philip¡¯s Work and Some New Results By Jun YU
  4. Forecasting Realized Volatility Using A Nonnegative Semiparametric Model By Daniel PREVE; Anders ERIKSSON; Jun YU

  1. By: Jan J.J. Groen (Federal Reserve Bank of New York); George Kapetanios (Queen Mary, University of London); Simon Price (Bank of England and City University)
    Abstract: Detection of structural change is a critical empirical activity, but continuous 'monitoring' of series, for structural changes in real time, raises well-known econometric issues that have been explored in a single series context. If multiple series co-break then it is possible that simultaneous examination of a set of series helps identify changes with higher probability or more rapidly than when series are examined on a case-by-case basis. Some asymptotic theory is developed for maximum and average CUSUM detection tests. Monte Carlo experiments suggest that these both provide an improvement in detection relative to a univariate detector over a wide range of experimental parameters, given a sufficiently large number of co-breaking series. This is robust to a cross-sectional correlation in the errors (a factor structure) and heterogeneity in the break dates. We apply the test to a panel of UK price indices.
    Keywords: Monitoring, Structural change, Panel, CUSUM, Fluctuation test
    JEL: C10 C59
    Date: 2010–02
  2. By: Giuseppe Cavaliere (Department of Statistical Sciences, University of Bologna); Anders Rahbek (Department of Economics, University of Copenhagen); A. M. Robert Taylor (School of Economics, University of Nottingham)
    Abstract: Determining the co-integrating rank of a system of variables has become a fundamental aspect of applied research in macroeconomics and finance. It is wellknown that standard asymptotic likelihood ratio tests for co-integration rank of Johansen (1996) can be unreliable in small samples with empirical rejection frequencies often very much in excess of the nominal level. As a consequence, bootstrap versions of these tests have been developed. To be useful, however, sequential procedures for determining the co-integrating rank based on these bootstrap tests need to be consistent, in the sense that the probability of selecting a rank smaller than (equal to) the true co-integrating rank will converge to zero (one minus the marginal significance level), as the sample size diverges, for general I(1) processes. No such likelihood-based procedure is currently known to be available. In this paper we fill this gap in the literature by proposing a bootstrap sequential algorithm which we demonstrate delivers consistent cointegration rank estimation for general I(1) processes. Finite sample Monte Carlo simulations show the proposed procedure performs well in practice.
    Keywords: co-integration; trace test; sequential rank determination; i.i.d. bootstrap; wild bootstrap
    JEL: C30 C32
    Date: 2010–02
  3. By: Jun YU (School of Economics, Singapore Management University)
    Abstract: Econometric analysis of continuous time models has drawn the attention of Peter Phillips for nearly 40 years, resulting in many important publications by him. In these publications he has dealt with a wide range of continuous time models and econometric problems, from univariate equations to systems of equations, from asymptotic theory to nite sample issues, from parametric models to nonparametric models, from identication problems to estimation and inference problems, from stationary models to nonstationary and nearly nonstationary models. This paper provides an overview of Peter Phillips' contributions in the continuous time econometrics literature. We review the problems that have been tackled by him, outline the main techniques suggested by him, and discuss the main results obtained by him. Based on his early work, we compare the performance of two asymptotic distributions in a simple setup. Results indicate that the in-ll asymptotics signicantly outperforms the long-span asymptotics.
    JEL: C22 C32
    Date: 2009–11
  4. By: Daniel PREVE (School of Economics, Singapore Management University); Anders ERIKSSON (Department of Information Science/Statistics, University of Uppsala); Jun YU (School of Economics, Singapore Management University)
    Abstract: This paper introduces a parsimonious and yet flexible nonnegative semiparametric model to forecast financial volatility. The new model extends the linear nonnegative autoregressive model of Barndorff-Nielsen & Shephard (2001) and Nielsen & Shephard (2003) by way of a power transformation. It is semiparametric in the sense that the dependency structure and distributional form of its error component are left unspecified. The statistical properties of the model are discussed and a novel estimation method is proposed. Simulation studies validate the new estimation method and suggest that it works reasonably well in finite samples. The out-of-sample performance of the proposed model is evaluated against a number of standard methods, using data on S&P 500 monthly realized volatilities. The competing models include the exponential smoothing method, a linear AR(1) model, a log-linear AR(1) model, and two long-memory ARFIMA models. Various loss functions are utilized to evaluate the predictive accuracy of the alternative methods. It is found that the new model generally produces highly competitive forecasts.
    Keywords: Autoregression, nonlinear/non-Gaussian time series, realized volatility, semiparametric model, volatility forecast.
    Date: 2009–11

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