nep-ets New Economics Papers
on Econometric Time Series
Issue of 2011‒06‒18
nine papers chosen by
Yong Yin
SUNY at Buffalo

  1. Long Memory Process in Asset Returns with Multivariate GARCH innovations By Imene Mootamri
  2. A New Procedure For Multiple Testing Of Econometric Models By Maxwell L. King; Xibin Zhang; Muhammad Akram
  3. A survey of functional principal component analysis By Han Lin Shang
  4. A comparative analysis of alternative univariate time series models in forecasting Turkish inflation By Catik, A. Nazif; Karaçuka, Mehmet
  5. Nonparametric Rank Tests for Non-stationary Panels By Pedroni, Peter; Vogelsang, Timothy J.; Wagner, Martin; Westerlund, Joakim
  6. Inference for VARs identified with sign restrictions By Hyungsik Roger Moon; Frank Schorfheide; Eleonara Granziera; Mihye Lee
  7. Covariate Unit Root Tests with Good Size and Power By Fossati, Sebastian
  8. An estimator for the quadratic covariation of asynchronously observed Itô processes with noise: Asymptotic distribution theory By Markus Bibinger
  9. Asymptotics of Asynchronicity By Markus Bibinger

  1. By: Imene Mootamri (GREQAM - Groupement de Recherche en Économie Quantitative d'Aix-Marseille - Université de la Méditerranée - Aix-Marseille II - Université Paul Cézanne - Aix-Marseille III - Ecole des Hautes Etudes en Sciences Sociales (EHESS) - CNRS : UMR6579)
    Abstract: The main purpose of this paper is to consider the multivariate GARCH (MGARCH) framework to model the volatility of a multivariate process exhibiting long term dependence in stock returns. More precisely, the long term dependence is examined in the …first conditional moment of US stock returns through multivariate ARFIMA process and the time-varying feature of volatility is explained by MGARCH models. An empirical application to the returns series is carried out to illustrate the usefulness of our approach. The main results confi…rm the presence of long memory property in the conditional mean of all stock returns.
    Keywords: Forecasting; Long memory; Multivariate GARCH; Stock Returns
    Date: 2011–06–09
  2. By: Maxwell L. King; Xibin Zhang; Muhammad Akram
    Abstract: A significant role for hypothesis testing in econometrics involves diagnostic checking. When checking the adequacy of a chosen model, researchers typically employ a range of diagnostic tests, each of which is designed to detect a particular form of model inadequacy. A major problem is how best to control the overall probability of rejecting the model when it is true and multiple test statistics are used. This paper presents a new multiple testing procedure, which involves checking whether the calculated values of the diagnostic statistics are consistent with the postulated model being true. This is done through a combination of bootstrapping to obtain a multivariate kernel density estimator of the joint density of the test statistics under the null hypothesis and Monte Carlo simulations to obtain a p value using this kernel density. We prove that under some regularity conditions, the estimated p value of our test procedure is a consistent estimate of the true p value. The proposed testing procedure is applied to tests for autocorrelation in an observed time series, for normality, and for model misspecification through the information matrix. We find that our testing procedure has correct or nearly correct sizes and good powers, particular for more complicated testing problems. We believe it is the first good method for calculating the overall p value for a vector of test statistics based on simulation.
    Keywords: Bootstrapping, consistency, information matrix test, Markov chain Monte Carlo simulation, multivariate kernel density, normality, serial correlation, test vector
    Date: 2011–05–25
  3. By: Han Lin Shang
    Abstract: Advances in data collection and storage have tremendously increased the presence of functional data, whose graphical representations are curves, images or shapes. As a new area of Statistics, functional data analysis extends existing methodologies and theories from the fields of functional analysis, generalized linear models, multivariate data analysis, nonparametric statistics and many others. This paper provides a review into functional data analysis with main emphasis on functional principal component analysis, functional principal component regression, and bootstrap in functional principal component regression. Recent trends as well as open problems in the area are discussed.
    Keywords: Bootstrap, functional principal component regression, functional time series, Stiefel manifold, Von Mise-Fisher distribution.
    Date: 2011–05
  4. By: Catik, A. Nazif; Karaçuka, Mehmet
    Abstract: This paper analyses inflation forecasting power of artificial neural networks with alternative univariate time series models for Turkey. The forecasting accuracy of the models is compared in terms of both static and dynamic forecasts for the period between 1982:1 and 2009:12. We find that at earlier forecast horizons conventional models, especially ARFIMA and ARIMA, provide better one-step ahead forecasting performance. However, unobserved components model turns out to be the best performer in terms of dynamic forecasts. The superiority of the unobserved components model suggests that inflation in Turkey has time varying pattern and conventional models are not able to track underlying trend of inflation in the long run. --
    Keywords: Inflation forecasting,Neural networks,Unobserved components model
    JEL: C45 C53 E31 E37
    Date: 2011
  5. By: Pedroni, Peter (Williams College, Williamstown, USA); Vogelsang, Timothy J. (Department of Economics, Michigan State University, East Lansing, USA); Wagner, Martin (Department of Economics and Finance, Institute for Advanced Studies, Vienna, Austria, and Frisch Centre for Economic Research, Oslo); Westerlund, Joakim (Department of Economics, University of Gothenburg, Sweden)
    Abstract: This study develops new rank tests for panels that include panel unit root tests as a special case. The tests are unusual in that they can accommodate very general forms of both serial and cross-sectional dependence, including cross-unit cointegration, without the need to specify the form of dependence or estimate nuisance parameters associated with the dependence. The tests retain high power in small samples, and in contrast to other tests that accommodate cross-sectional dependence, the limiting distributions are valid for panels with finite cross-sectional dimensions.
    Keywords: Nonparametric rank tests, unit roots, cointegration, cross-sectional dependence
    JEL: C12 C22 C23
    Date: 2011–06
  6. By: Hyungsik Roger Moon; Frank Schorfheide; Eleonara Granziera; Mihye Lee
    Abstract: There is a fast growing literature that partially identifies structural vector autoregressions (SVARs) by imposing sign restrictions on the responses of a subset of the endogenous variables to a particular structural shock (sign-restricted SVARs). To date, the methods that have been used are only justified from a Bayesian perspective. This paper develops methods of constructing error bands for impulse response functions of sign-restricted SVARs that are valid from a frequentist perspective. The authors also provide a comparison of frequentist and Bayesian error bands in the context of an empirical application — the former can be twice as wide as the latter.
    Keywords: Vector autoregression ; Econometric models
    Date: 2011
  7. By: Fossati, Sebastian (University of Alberta, Department of Economics)
    Abstract: The selection of the truncation lag for covariate unit root tests is analyzed using Monte Carlo simulation. It is shown that standard information criteria such as the BIC or the AIC can result in tests with large size distortions. Modifi ed information criteria can be used to construct tests with good size and power. An empirical illustration is provided.
    Keywords: unit root tests; truncation lag; information criteria; vector autoregressions
    JEL: C12 C32 C52
    Date: 2011–05–01
  8. By: Markus Bibinger
    Abstract: The article is devoted to the nonparametric estimation of the quadratic covariation of non-synchronously observed Itô processes in an additive microstructure noise model. In a high-frequency setting, we aim at establishing an asymptotic distribution theory for a generalized multiscale estimator including a feasible central limit theorem with optimal convergence rate on convenient regularity assumptions. The inevitably remaining impact of asynchronous deterministic sampling schemes and noise corruption on the asymptotic distribution is precisely elucidated. A case study for various important examples, several generalizations of the model and an algorithm for the implementation warrant the utility of the estimation method in applications.
    Keywords: non-synchronous observations, microstructure noise, integrated covolatility, multiscale estimator, stable limit theorem
    JEL: C14 C32 G10
    Date: 2011–06
  9. By: Markus Bibinger
    Abstract: In this article we focus on estimating the quadratic covariation of continuous semimartingales from discrete observations that take place at asynchronous observation times. The Hayashi-Yoshida estimator serves as synchronized realized covolatility for that we give our own distinct illustration based on an iterative synchronization algorithm. We consider high-frequency asymptotics and prove a feasible stable central limit theorem. The characteristics of non-synchronous observation schemes affecting the asymptotic variance are captured by a notion of asymptotic covariations of times. These are precisely illuminated and explicitly deduced for the important case of independent time-homogeneous Poisson sampling.
    Keywords: non-synchronous observations, quadratic covariation, Hayashi-Yoshida estimator, stable limit theorem, asymptotic distribution
    JEL: C14 C32 G10
    Date: 2011–06

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