nep-ets New Economics Papers
on Econometric Time Series
Issue of 2012‒07‒14
five papers chosen by
Yong Yin
SUNY at Buffalo

  1. Nonlinearity, Breaks, and Long-Range Dependence in Time-Series Models By Eric Hillebrand; Marcelo C. Medeiros
  2. Asymptotic Theory for Regressions with Smoothly Changing Parameters By Eric Hillebrand; Marcelo C. Medeiros; Junyue Xu
  3. Bayesian semiparametric multivariate GARCH modeling By Mark J Jensen; John M Maheu
  4. Markov-switching dynamic factor models in real time By Maximo Camacho; Gabriel Perez-Quiros; Pilar Poncela
  5. Signal extraction for nonstationary multivariate time series with illustrations for trend inflation By Tucker S. McElroy; Thomas M. Trimbur

  1. By: Eric Hillebrand (Aarhus University and CREATES); Marcelo C. Medeiros (PONTIFICAL CATHOLIC UNIVERSITY OF RIO DE JANEIRO)
    Abstract: We study the simultaneous occurrence of long memory and nonlinear effects, such as parameter changes and threshold effects, in ARMA time series models and apply our modeling framework to daily realized volatility. Asymptotic theory for parameter estimation is developed and two model building procedures are proposed. The methodology is applied to stocks of the Dow Jones Industrial Average during the period 2000 to 2009. We find strong evidence of nonlinear effects.
    Keywords: Smooth transitions, long memory, forecasting, realized volatility.
    JEL: C22
    Date: 2012–06–12
  2. By: Eric Hillebrand (Aarhus University and CREATES); Marcelo C. Medeiros (PONTIFICAL CATHOLIC UNIVERSITY OF RIO DE JANEIRO); Junyue Xu (LOUISIANA STATE UNIVERSITY)
    Abstract: We derive asymptotic properties of the quasi maximum likelihood estimator of smooth transition regressions when time is the transition variable. The consistency of the estimator and its asymptotic distribution are examined. It is shown that the estimator converges at the usual square-root-of-T rate and has an asymptotically normal distribution. Finite sample properties of the estimator are explored in simulations. We illustrate with an application to US inflation and output data.
    Keywords: Regime switching, smooth transition regression, asymptotic theory.
    JEL: C22
    Date: 2012–06–12
  3. By: Mark J Jensen; John M Maheu
    Abstract: This paper proposes a Bayesian nonparametric modeling approach for the return distribution in multivariate GARCH models. In contrast to the parametric literature the return distribution can display general forms of asymmetry and thick tails. An infinite mixture of multivariate normals is given a flexible Dirichlet process prior. The GARCH functional form enters into each of the components of this mixture. We discuss conjugate methods that allow for scale mixtures and nonconjugate methods which provide mixing over both the location and scale of the normal components. MCMC methods are introduced for posterior simulation and computation of the predictive density. Bayes factors and density forecasts with comparisons to GARCH models with Student-t innovations demonstrate the gains from our flexible modeling approach.
    Keywords: Dirichlet process mixture, slice sampling
    JEL: C11 C14 C32 C53 C58
    Date: 2012–06–29
  4. By: Maximo Camacho (Universidad de Murcia); Gabriel Perez-Quiros (Banco de España and CEPR); Pilar Poncela (Universidad Autónoma de Madrid)
    Abstract: We extend the Markov-switching dynamic factor model to account for some of the specifi cities of the day-to-day monitoring of economic developments from macroeconomic indicators, such as ragged edges and mixed frequencies. We examine the theoretical benefi ts of this extension and corroborate the results through several Monte Carlo simulations. Finally, we assess its empirical reliability to compute real-time inferences of the US business cycle.
    Keywords: Business cycles, output growth, time series
    JEL: E32 C22 E27
    Date: 2012–02
  5. By: Tucker S. McElroy; Thomas M. Trimbur
    Abstract: This paper advances the theory and methodology of signal extraction by introducing asymptotic and finite sample formulas for optimal estimators of signals in nonstationary multivariate time series. Previous literature has considered only univariate or stationary models. However, in current practice and research, econometricians, macroeconomists, and policy-makers often combine related series - that may have stochastic trends--to attain more informed assessments of basic signals like underlying inflation and business cycle components. Here, we use a very general model structure, of widespread relevance for time series econometrics, including flexible kinds of nonstationarity and correlation patterns and specific relationships like cointegration and other common factor forms. First, we develop and prove the generalization of the well-known Wiener-Kolmogorov formula that maps signal-noise dynamics into optimal estimators for bi-infinite series. Second, this paper gives the first explicit treatment of finite-length multivariate time series, providing a new method for computing signal vectors at any time point, unrelated to Kalman filter techniques; this opens the door to systematic study of near end-point estimators/filters, by revealing how they jointly depend on a function of signal location and parameters. As an illustration we present econometric measures of the trend in total inflation that make optimal use of the signal content in core inflation.
    Date: 2012

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