nep-ets New Economics Papers
on Econometric Time Series
Issue of 2015‒04‒11
nine papers chosen by
Yong Yin
SUNY at Buffalo

  1. Inference and Testing Breaks in Large Dynamic Panels with Strong Cross Sectional Dependence By Javier Hidalgo; Marcia M Schafgans
  2. Estimation of long memory in volatility using wavelets By Kraicova, Lucie; Barunik, Jozef
  3. Modeling and forecasting persistent financial durations By Zikes, Filip; Barunik, Jozef; Shenai, Nikhil
  4. A simple model for now-casting volatility series By Hafner, Christian M.; Breitung, Jörg
  5. Linearity and misspecification tests for vector smooth transition regression models By Terasvirta, Timo; Yang, Yukai
  6. Specification, estimation and evaluation of vector smooth transition autoregressive models with applications By Terasvirta, Timo; Yang, Yukai
  7. Autoregressive moving average infinite hidden markov-switching models By Bauwens, Luc; Carpantier, Jean-François; Dufays, Arnaud
  8. Structural Vector Autoregressions with Heteroskedasticity: A Comparison of Different Volatility Models By Helmut Lütkepohl; Aleksei Netsunajev
  9. FloGARCH: Realizing Long Memory and Asymmetries in Returns Valitility By Harry-Paul Vander Elst

  1. By: Javier Hidalgo; Marcia M Schafgans
    Abstract: This paper is concerned with various issues related to inference in large dynamic panel data models (where both n and T increase without bound) in the presence of, possibly, strong cross-sectional dependence. Our first aim is to provide a Central Limit Theorem for estimators of the slope parameters of the model under mild conditions. To that end, we extend and modify existing results available in the literature. Our second aim is to study two, although similar, tests for breaks/homogeneity in the time dimension. The first test is based on the CUSUM principle; whereas the second test is based on a Hausman-Durbin-Wu approach. Some of the key features of the tests are that they have nontrivial power when the number of individuals, for which the slope parameters may differ, is a "negligible" fraction or when the break happens to be towards the end of the sample. Due to the fact that the asymptotic distribution of the tests may not provide a good approximation for their finite sample distribution, we describe a simple bootstrap algorithm to obtain (asymptotic) valid critical values for our statistics. An important and surprising feature of the bootstrap is that there is no need to know the underlying model of the cross-sectional dependence, and hence the bootstrap does not require to select any bandwidth parameter for its implementation, as is the case with moving block bootstrap methods which may not be valid with cross-sectional dependence and may depend on the particular ordering of the individuals. Finally, we present a Monte-Carlo simulation analysis to shed some light on the small sample behaviour of the tests and their bootstrap analogues.
    Keywords: Large panel data, dynamic models, cross-sectional strong-dependence, central limit theorems, homogeneity, bootstrap algorithms
    JEL: C12 C13 C23
    Date: 2015–04
  2. By: Kraicova, Lucie; Barunik, Jozef
    Abstract: This work studies wavelet-based Whittle estimator of the Fractionally Integrated Exponential Generalized Autoregressive Conditional Heteroscedasticity (FIEGARCH) model, often used for modeling long memory in volatility of financial assets. The newly proposed estimator approximates the spectral density using wavelet transform, which makes it more robust to certain types of irregularities in data. Based on an extensive Monte Carlo study, both behaviour of the proposed estimator and its relative performance with respect to traditional estimators are assessed. In addition, we study properties of the estimators in presence of jumps, which brings interesting discussion. We find that wavelet-based estimator may become an attractive robust and fast alternative to the traditional methods of estimation.
    Keywords: volatility,long memory,FIEGARCH,wavelets,Whittle,Monte Carlo
    Date: 2015
  3. By: Zikes, Filip; Barunik, Jozef; Shenai, Nikhil
    Abstract: This paper introduces the Markov-Switching Multifractal Duration (MSMD) model by adapting the MSM stochastic volatility model of Calvet and Fisher (2004) to the duration setting. Although the MSMD process is exponential ß-mixing as we show in the paper, it is capable of generating highly persistent autocorrelation. We study analytically and by simulation how this feature of durations generated by the MSMD process propagates to counts and realized volatility. We employ a quasi-maximum likelihood estimator of the MSMD parameters based on the Whittle approximation and establish its strong consistency and asymptotic normality for general MSMD specifications. We show that the Whittle estimation is a computationally simple and fast alternative to maximum likelihood. Finally, we compare the performance of the MSMD model with competing short- and long-memory duration models in an out-of-sample forecasting exercise based on price durations of three major foreign exchange futures contracts. The results of the comparison show that the MSMD and the Long Memory Stochastic Duration model perform similarly and are superior to the short-memory Autoregressive Conditional Duration models.
    Keywords: price durations,long memory,multifractal models,realized volatility,Whittle estimation
    JEL: C13 C58 G17
    Date: 2015
  4. By: Hafner, Christian M. (Université catholique de Louvain, CORE, Belgium); Breitung, Jörg (University of Cologne)
    Abstract: Nowcasting volatility of financial time series appears difficult with classical volatility models. This paper proposes a simple model, based on an ARMA representation of the log-transformed squared returns, that allows to estimate current volatility, given past and current returns, in a very simple way. The model can be viewed as a degenerate case of the stochastic volatility model with perfect correlation between the two error terms. It is shown that the volatility nowcasts do not depend on this correlation, so that both models provide the same nowcasts for given parameter values. A simulation study suggests that the ARMA and SV models have a similar performance, but that in cases of moderate persistence the ARMA model is preferable. An extension of the ARMA model is proposed that takes into account the so-called leverage effect. Finally, the alternative models are applied to a long series of daily S&P 500 returns.
    Keywords: EGARCH, stochastic volatility, ARMA, realized volatility
    JEL: C22 C58
    Date: 2014–11–19
  5. By: Terasvirta, Timo (Aarhus University); Yang, Yukai (Université catholique de Louvain, CORE, Belgium)
    Abstract: In this paper, we derive Lagrange multiplier and Lagrange multiplier type specification and misspecification tests for vector smooth transition models. We report results from simulation studies in which the size and power properties of the proposed tests in small samples are considered. The results show that these asymptotic tests generally suffer from size distortion. We find that Wilks’s and Rao’s F statistic both have satisfactory size properties and can be recommended for empirical use. Bootstrapping the standard asymptotic LM statistic offers another solution to the problem.
    Keywords: Vector STAR models, linearity test, misspecification test, vector nonlinear time series, serial correlation, parameter constancy, residual nonlinearity test
    JEL: C12 C32 C52
    Date: 2014–11–30
  6. By: Terasvirta, Timo (Aarhus University); Yang, Yukai (Université catholique de Louvain, CORE, Belgium)
    Abstract: We consider a nonlinear vector model called the logistic vector smooth transition autoregressive model. The bivariate single-transition vector smooth transition regression model of Camacho (2004) is generalised to a multivariate and multitransition one. A modelling strategy consisting of specification, including testing linearity, estimation and evaluation of these models is constructed. Nonlinear least squares estimation of the parameters of the model is discussed. Evaluation by misspecification tests is carried out using tests derived in a companion paper. The use of the modelling strategy is illustrated by two applications. In the first one, the dynamic relationship between the US gasoline price and consumption is studied and possible asymmetries in it considered. The second application consists of modelling two well known Icelandic riverflow series, previously considered by many hydrologists and time series analysts.
    Keywords: Vector STAR models, modelling nonlinearity, vector autoregression, generalized impulse response, asymmetry, oil price, river flow
    JEL: C32 C51 C52
    Date: 2014–11–30
  7. By: Bauwens, Luc (Université catholique de Louvain, CORE, Belgium); Carpantier, Jean-François (CREA, University of Luxembourg); Dufays, Arnaud (Université catholique de Louvain, CORE, Belgium)
    Abstract: Markov-switching models are usually specified under the assumption that all the parameters change when a regime switch occurs. Relaxing this hypothesis and being able to detect which parameters evolve over time is relevant for interpreting the changes in the dynamics of the series, for specifying models parsimoniously, and may be helpful in forecasting. We propose the class of sticky infinite hidden Markov-switching autoregressive moving average models, in which we disentangle the break dynamics of the mean and the variance parameters. In this class, the number of regimes is possibly infinite and is determined when estimating the model, thus avoiding the need to set this number by a model choice criterion. We develop a new Markov chain Monte Carlo estimation method that solves the path dependence issue due to the moving average component. Empirical results on macroeconomic series illustrate that the proposed class of models dominates the model with fixed parameters in terms of point and density forecasts.
    Keywords: ARMA, Bayesian inference, Dirichlet process, Forecasting, Marko v-switching
    JEL: C11 C15 C22 C53 C58
    Date: 2015–02–13
  8. By: Helmut Lütkepohl; Aleksei Netsunajev
    Abstract: A growing literature uses changes in residual volatility for identifying structural shocks in vector autoregressive (VAR) analysis. A number of different models for heteroskedasticity or conditional heteroskedasticity are proposed and used in applications in this context. This study reviews the different volatility models and points out their advantages and drawbacks. It thereby enables researchers wishing to use identification of structural VAR models via heteroskedasticity to make a more informed choice of a suitable model for a specific empirical analysis. An application investigating the interaction between U.S. monetary policy and the stock market is used to illustrate the related issues.
    Keywords: Structural vector autoregression, identification via heteroskedasticity, conditional heteroskedasticity, smooth transition, Markov switching, GARCH
    JEL: C32
    Date: 2015
  9. By: Harry-Paul Vander Elst
    JEL: C22 C53
    Date: 2015–04

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