nep-ets New Economics Papers
on Econometric Time Series
Issue of 2013‒11‒09
five papers chosen by
Yong Yin
SUNY at Buffalo

  1. Detrending moving-average cross-correlation coefficient: Measuring cross-correlations between non-stationary series By Ladislav Kristoufek
  2. Temporal aggregation of univariate and multivariate time series models: A survey By SILVESTRINI, Andrea; VEREDAS, David
  3. A moment-matching method for approximating vector autoregressive processes by finite-state Markov chains By Nikolay Gospodinov; Damba Lkhagvasuren
  4. A unified framework for testing in the linear regression model under unknown order of fractional integration By Christensen, Bent Jesper; Kruse, Robinson; Sibbertsen, Philipp
  5. Adaptive quadrature for likelihood inference on dynamic latent variable models for time-series and panel data By Cagnone, Silvia; Bartolucci, Francesco

  1. By: Ladislav Kristoufek
    Abstract: In the paper, we introduce a new measure of correlation between possibly non-stationary series. As the measure is based on the detrending moving-average cross-correlation analysis (DMCA), we label it as the DMCA coefficient $\rho_{DMCA}(\lambda)$ with a moving average window length $\lambda$. We analytically show that the coefficient ranges between -1 and 1 as a standard correlation does. In the simulation study, we show that the values of $\rho_{DMCA}(\lambda)$ very well correspond to the true correlation between the analyzed series regardless the (non-)stationarity level. Dependence of the newly proposed measure on other parameters -- correlation level, moving average window length and time series length -- is discussed as well.
    Date: 2013–11
  2. By: SILVESTRINI, Andrea; VEREDAS, David
  3. By: Nikolay Gospodinov; Damba Lkhagvasuren
    Abstract: This paper proposes a moment-matching method for approximating vector autoregressions by finite-state Markov chains. The Markov chain is constructed by targeting the conditional moments of the underlying continuous process. The proposed method is more robust to the number of discrete values and tends to outperform the existing methods for approximating multivariate processes over a wide range of the parameter space, especially for highly persistent vector autoregressions with roots near the unit circle.
    Date: 2013
  4. By: Christensen, Bent Jesper; Kruse, Robinson; Sibbertsen, Philipp
    Abstract: We consider hypothesis testing in a general linear time series regression framework when the possibly fractional order of integration of the error term is unknown. We show that the approach suggested by Vogelsang (1998a) for the case of integer integration does not apply to the case of fractional integration. We propose a Lagrange Multiplier-type test whose limiting distribution is independent of the order of integration of the errors. Different testing scenarios for the case of deterministic and stochastic regressors are considered. Simulations demonstrate that the proposed test works well for a variety of different cases, thereby emphasizing its generality.
    Keywords: Long memory; linear time series regression; Lagrange Multiplier test
    JEL: C12 C22
    Date: 2013–10
  5. By: Cagnone, Silvia; Bartolucci, Francesco
    Abstract: Maximum likelihood estimation of dynamic latent variable models requires to solve integrals that are not analytically tractable. Numerical approximations represent a possible solution to this problem. We propose to use the Adaptive Gaussian-Hermite (AGH) numerical quadrature approximation for a class of dynamic latent variable models for time-series and panel data. These models are based on continuous time-varying latent variables which follow an autoregressive process of order 1, AR(1). Two examples of such models are the stochastic volatility models for the analysis of financial time-series and the limited dependent variable models for the analysis of panel data. A comparison between the performance of AGH methods and alternative approximation methods proposed in the literature is carried out by simulation. Examples on real data are also used to illustrate the proposed approach.
    Keywords: AR(1); categorical longitudinal data; Gaussian-Hermite quadrature; limited dependent variable models; stochastic volatility model
    JEL: C13 C32 C33
    Date: 2013–10–29

This nep-ets issue is ©2013 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.