nep-ets New Economics Papers
on Econometric Time Series
Issue of 2014‒02‒02
sixteen papers chosen by
Yong Yin
SUNY at Buffalo

  1. Long- versus medium-run identification in fractionally integrated VAR models By Tschernig, Rolf; Weber, Enzo; Weigand, Roland
  2. The Asymptotic Size and Power of the Augmented Dickey-Fuller Test for a Unit Root By Paparoditis, Efstathios; Politis, Dimitris N
  3. Efficient Iterative Maximum Likelihood Estimation of High-Parameterized Time Series Models By Nikolaus Hautsch; Ostap Okhrin; Alexander Ristig;
  4. Tests of Financial Market Contagion- Evolutionary Cospectral Analysis V.S. Wavelet Analysis By Zied Ftiti; Aviral Tiwari; Amél Belanès
  5. Conditional Correlations and Volatility Spillovers between Oil Price and OECD Stock index: a Multivariate Analysis By Anna Creti; Khaled Guesmi; Ilyes Abid
  6. Semiparametric Generalized Long Memory Modelling of GCC Stock Market Returns: A Wavelet Approach By Heni Boubaker; Nadia Sghaier
  7. Testing for common cycles in non-stationary VARs with varied frecquency data By Hecq A.W.; Urbain J.R.Y.J.; Götz T.B.
  8. Nowcasting causality in mixed frequency vector autoregressive models By Götz T.B.; Hecq A.W.
  9. A Lagrange multiplier-type test for idiosyncratic unit roots in the exact factor model under misspecification By Solberger M.; Zhou X.
  10. LM-type tests for idiosyncratic and common unit roots in the exact factor model with AR(1) dynamics By Zhou X.; Solberger M.
  11. Robust block bootstrap panel predictability tests By Westerlund J.; Smeekes S.
  12. Direct Versus Indirect Approach in Seasonal Adjustment By Marcus Scheiblecker
  13. Anticipating business-cycle turning points in real time using density forecasts from a VAR By Schreiber, Sven
  14. Forecasting business-cycle turning points with (relatively large) linear systems in real time By Schreiber, Sven
  15. Nonstationary-Volatility Robust Panel Unit Root Tests and the Great Moderation By Czudaj, Robert; Hanck, Christoph
  16. A latent dynamic factor approach to forecasting multivariate stock market volatility By Gribisch, Bastian

  1. By: Tschernig, Rolf; Weber, Enzo; Weigand, Roland
    Abstract: We state that long-run restrictions that identify structural shocks in VAR models with unit roots lose their original interpretation if the fractional integration order of the affected variable is below one. For such fractionally integrated models we consider a medium-run approach that employs restrictions on variance contributions over finite horizons. We show for alternative identification schemes that letting the horizon tend to infinity is equivalent to imposing the restriction of Blanchard and Quah (1989) introduced for the unit-root case.
    Keywords: Structural vector autoregression; long-run restriction; finite-horizon identification; fractional integration; impulse response function
    JEL: C32 C50
    Date: 2014–02
  2. By: Paparoditis, Efstathios; Politis, Dimitris N
    Abstract: It is shown that the limiting distribution of the augmented Dickey-Fuller (ADF) test under the null hypothesis of a unit root is valid under a very general set of assumptions that goes far beyond the linear AR (∞) process assumption typically imposed. In essence, all that is required is that the error process driving the random walk possesses a spectral density that is strictly positive. Given that many economic time series are nonlinear, this extended result may have important applications. Furthermore, under the same weak assumptions, the limiting distribution of the ADF test is derived under the alternative of stationarity, and a theoretical explanation is given for the well-known empirical fact that the test's power is a decreasing function of the autoregressive order p used in the augmented regression equation. The intuitive reason for the reduced power of the ADF test as p tends to infinity is that the p regressors become asymptotically collinear.  
    Keywords: Social and Behavioral Sciences, Autoregressive Representation, Hypothesis Testing, Integrated Series, Unit Root
    Date: 2013–12–01
  3. By: Nikolaus Hautsch; Ostap Okhrin; Alexander Ristig;
    Abstract: We propose an iterative procedure to efficiently estimate models with complex log-likelihood functions and the number of parameters relative to the observations being potentially high. Given consistent but inefficient estimates of sub-vectors of the parameter vector, the procedure yields computationally tractable, consistent and asymptotic efficient estimates of all parameters. We show the asymptotic normality and derive the estimator's asymptotic covariance in dependence of the number of iteration steps. To mitigate the curse of dimensionality in high-parameterized models, we combine the procedure with a penalization approach yielding sparsity and reducing model complexity. Small sample properties of the estimator are illustrated for two time series models in a simulation study. In an empirical application, we use the proposed method to estimate the connectedness between companies by extending the approach by Diebold and Yilmaz (2014) to a high-dimensional non-Gaussian setting.
    Keywords: Multi-Step estimation, Sparse estimation, Multivariate time series, Maximum likelihood estimation, Copula
    JEL: C13 C32 C50
    Date: 2014–01
  4. By: Zied Ftiti; Aviral Tiwari; Amél Belanès
    Abstract: This paper examines the co-movements dynamics between OCDE countries with the US and Europe. The core focus is to suggest advantageous techniques allowing the investigation with respect to time and frequency, namely evolutionary co-spectral analysis and wavelet analysis. Our study puts in evidence the existence of both long run and short-run co-movements. Both interdependence and contagion are well identified across markets; but with slight differences. Both investors and policymakers can derive worthwhile information from this research. Recognizing countries sensitivity to permanent and transitory shocks enables investors to select rational investment strategies. Similarly, policymakers can make safe crisis management policies.
    Keywords: contagion, interdependence, stock markets index, evolutionary co-spectral analysis, wavelet analysis.
    Date: 2014–01–06
  5. By: Anna Creti; Khaled Guesmi; Ilyes Abid
    Abstract: This paper aims to explore the links between Brent crude oil index and stock markets index in OECD countries. We estimate time-varying conditional correlation relationships among these variables by employing a Multivariate Fractionally Integrated Asymmetric, Power ARCH model with dynamic corrected conditional correlations of Engle (1982) M-FIAPARCH-c-DCCE with a Student-t distribution. This process detects eventual volatility spillovers, asymmetries and persistence, which are typically observed in stock markets and oil prices. Our sample consists of monthly frequency stock indexes and oil price, covering 17 OECD countries for the period January, 1990- September, 2012. We find that at the beginning of our sample, oil has offered diversification opportunities with respect to the stock market, but this trend has been reversed in the last decade. We regroup the countries sample in 5 groups which present quite similar patterns of dynamic correlation between oil and their stock market and corroborate our geographical clustering by multivariate correlations among stock markets.
    Keywords: Multivariate Fractional Cointegration, Oil Prices, stock markets, M-FIAPARCH-c-DCCE.
    JEL: C10 E44 G15
    Date: 2014–01–06
  6. By: Heni Boubaker; Nadia Sghaier
    Abstract: This paper proposes a new class of semiparametric generalized long memory model with FIA- PARCH errors (SEMIGARMA-FIAPARCH model) that extends the conventionnel GARMA model to incorporate nonlinear deterministic trend, in the mean equation, and to allow for time varying volatility, in the conditional variance equation. The parameters of this model are estimated in a wavelet domain. We provide an empirical application of this model to examine the dynamic of the stock market returns in six GCC countries. The empirical results show that the model proposed o¤ers an interesting framework to describe the seasonal long range dependence and the nonlinear deterministic trend in the return as well as persistence to shocks in the conditional volatiliy. We also compare its performance predictive to the traditional long memory model with FIAPARCH errors (FARMA-FIAPARCH model). The predictive results indicate that the model proposed out performs the FARMA-FIAPARCH model.
    Keywords: semiparametric generalized long memory process, FIAPARCH errors, wavelet do- main, stock market returns.
    JEL: C13 C22 C32 G15
    Date: 2014–01–06
  7. By: Hecq A.W.; Urbain J.R.Y.J.; Götz T.B. (GSBE)
    Abstract: This paper proposes a new way for detecting the presence of common cyclical featureswhen several time series are observed/sampled at different frequencies, hence generalizingthe common-frequency approach introduced by Engle and Kozicki 1993 and Vahid andEngle 1993. We start with the mixed-frequency VAR representation investigated in Ghysels2012 for stationary time series. For non-stationary time series in levels, we showthat one has to account for the presence of two sets of long-run relationships. The First setis implied by identities stemming from the fact that the differences of the high-frequencyI1 regressors are stationary. The second set comes from possible additional long-run relationshipsbetween one of the high-frequency series and the low-frequency variables. Ourtransformed VECM representations extend the results of Ghysels 2012 and are very importantfor determining the correct set of variables to be used in a subsequent commoncycle investigation. This has some empirical implications both for the behavior of the teststatistics as well as for forecasting. Empirical analyses with the quarterly real GNP andmonthly industrial production indices for, respectively, the U.S. and Germany illustrate ournew approach. This is also investigated in a Monte Carlo study, where we compare our proposedmixed-frequency models with models stemming from classical temporal aggregationmethods.
    Keywords: Economic History: Transport, Trade, Energy, Technology, and Other Services: Asia including Middle East; Regional and Urban History: General; Microeconomic Analyses of Economic Development;
    JEL: N90 O12 N75
    Date: 2013
  8. By: Götz T.B.; Hecq A.W. (GSBE)
    Abstract: This paper introduces the notion of nowcasting causality for mixed-frequency VARs as the mixed-frequency version of instantaneous causality. We analyze the relationship between nowcasting and Granger causality in the mixed-frequency VAR setting of Ghysels 2012 and illustrate that nowcasting causality can have a crucial impact on the significance of contemporaneous or lagged high-frequency variables in standard MIDAS regression models.
    Keywords: Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models;
    JEL: C21 C22 C32
    Date: 2013
  9. By: Solberger M.; Zhou X. (GSBE)
    Abstract: We consider an exact factor model and derive a Lagrange multiplier-type test for unit roots in the idiosyncratic components. The asymptotic distribution of the statistic is derived under the misspecification that the differenced factors are white noise. We prove that the asymptotic distribution is independent of the distribution of the factors, and that the factors are allowed to be integrated, cointegrate, or be stationary. In a simulation study, size and power is compared with some popular second generation panel unit root tests. The simulations suggest that our statistic is well-behaved in terms of size and that it is powerful and robust in comparison with existing tests.
    Keywords: Hypothesis Testing: General; Single Equation Models; Single Variables: Models with Panel Data; Longitudinal Data; Spatial Time Series;
    JEL: C12 C23
    Date: 2013
  10. By: Zhou X.; Solberger M. (GSBE)
    Abstract: Recent developments within the panel unit-root literature have illustrated how the exact factor model serves as a parsimonious framework and allows for consistent maximum likelihood inference even when it is misspecified contra the more general approximate factor model. In this paper we consider an exact factor model with AR1 dynamics and propose LM-type tests for idiosyncratic and common unit roots. We derive the asymptotic distributions and carry out simulations to investigate size and power of the tests in finite samples, as well as compare the performance with some existing tests.
    Keywords: Hypothesis Testing: General; Single Equation Models; Single Variables: Models with Panel Data; Longitudinal Data; Spatial Time Series;
    JEL: C12 C23
    Date: 2013
  11. By: Westerlund J.; Smeekes S. (GSBE)
    Abstract: Most panel data studies of the predictability of returns presume that the cross-sectional units are independent, an assumption that is not realistic. As a response to this, the current paper develops block bootstrap-based panel predictability tests that are valid under very general conditions. Some of the allowable features include heterogeneous predictive slopes, persistent predictors, and complex error dynamics, including cross-unit endogeneity.
    Keywords: Statistical Simulation Methods: General; Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Single Equation Models; Single Variables: Models with Panel Data; Longitudinal Data; Spatial Time Series; Financial Crises; Asset Pricing; Trading volume; Bond Interest Rates;
    JEL: C15 C22 C23 G01 G12
    Date: 2013
  12. By: Marcus Scheiblecker (WIFO)
    Abstract: With seasonal adjustment one has to decide whether to seasonal adjust an aggregate like GDP directly or to sum up its seasonally adjusted components. This choice is usually driven by subjective motives or practical convenience. In the case of seasonal adjustment with chain-linked data one might feel forced to use the direct approach as components do not even add up to aggregates before the adjustment. This paper presents a guide for practitioners, which recommends a more objective way of decision-making, based on several indicators. It proposes some of these criteria which can facilitate the decision between the direct and the indirect approach. For the case of chain-linked series, where the indirect approach seems not to be feasible because components are not adding up to an aggregate, the paper presents a method how the indirect approach of seasonal adjustment nevertheless can be applied. Finally it deals with a possible balancing process between the results of the direct and the indirect approach and a practical application example is given.
    Keywords: Seasonal adjustment, direct indirect method, chain-linking
    Date: 2014–01–29
  13. By: Schreiber, Sven
    Abstract: For the timely detection of business-cycle turning points we suggest to use mediumsized linear systems (subset VARs with automated zero restrictions) to forecast the relevant underlying variables, and to derive the probability of the turning point from the forecast density as the probability mass below (or above) a given threshold value. We show how this approach can be used in real time in the presence of data publication lags and how it can capture the part of the data revision process that is systematic. Then we apply the method to US and German monthly data. In an out-of-sample exercise (for 2007-2012/13) the turning points can be signalled before the official data publication confirms them (but not before they happened in reality). --
    Keywords: density forecasts,business-cycle turning points,real-time data,nowcasting,great recession
    JEL: C53 E37
    Date: 2014
  14. By: Schreiber, Sven
    Abstract: The detection of business-cycle turning points is usually performed with non-linear discrete-regime models such as binary dependent variable (e.g., probit or logit) or Markov-switching methods. The probit model has the drawback that the continuous underlying target variable is discretized, with a considerable loss of information. The Markov-switching approach in general presupposes a non-linear data-generating process, and the numerical likelihood maximization becomes increasingly dif cult when more covariates are used. To avoid these problems we suggest to rst use standard linear systems (subset VARs with zero restrictions) to forecast the relevant underlying variable(s), and in a second step to derive the probability of a suitably de ned turning point from the forecast probability density function. This approach will never fail numerically. We also discuss and show how this approach can be used in real time in the presence of publication lags and to capture features of the data revision process, and we apply the method to German data; the event of the recent Great Recession is rst signalled in June 2008, several months before the of cial published data con rms it (but due to publication and recognition lags it is found after it already began in reality). --
    JEL: C53 E37 E32
    Date: 2013
  15. By: Czudaj, Robert; Hanck, Christoph
    Abstract: This paper argues that typical applications of panel unit root tests should take possible nonstationarity in the volatility process of the innovations of the panel time series into account. Nonstationarity volatility arises for instance when there are structural breaks in the innovation variances. A prominent example is the reduction in GDP growth variances enjoyed by many industrialized countries, known as the `Great Moderation.' It also proposes a new testing approach for panel unit roots that is, unlike many previously suggested tests, robust to such volatility processes. The panel test is based on Simes' [Biometrika 1986, "An Improved Bonferroni Procedure for Multiple Tests of Signi cance"] classical multiple test, which combines evidence from time series unit root tests of the series in the panel. As time series unit root tests, we employ recently proposed tests of Cavaliere and Taylor [Journal of Time Series Analysis 2008b, "Time-Transformed Unit Root Tests for Models with Non-Stationary Volatility"]. The panel test is robust to general patterns of cross-sectional dependence and yet is straightforward to implement, only requiring valid p-values of time series unit root tests, and no resampling. Monte Carlo experiments show that other panel unit root tests suffer from sometimes severe size distortions in the presence of nonstationary volatility, and that this defect can be remedied using the test proposed here. We use the methods developed here to test for unit roots in OECD panels of gross domestic products and inflation rates, yielding inference robust to the `Great Moderation.' We find little evidence of trend stationarity, and mixed evidence regarding inflation stationarity. --
    JEL: C12 C23 E31
    Date: 2013
  16. By: Gribisch, Bastian
    Abstract: This paper proposes a latent dynamic factor model for low- as well as high-dimensional realized covariance matrices of stock returns. The approach is based on the matrix logarithm and allows for flexible dynamic dependence patterns by combining common latent factors driven by HAR dynamics and idiosyncratic AR(1) factors. The model accounts for symmetry and positive definiteness of covariance matrices without imposing parametric restrictions. Simulated Bayesian parameter estimates as well as positive definite (co)variance forecasts are obtained using Markov Chain Monte Carlo (MCMC) methods. An empirical application to 5-dimensional and 30-dimensional realized covariance matrices of daily New York Stock Exchange (NYSE) stock returns shows that the model outperforms other approaches of the extant literature both in-sample and out-of-sample. --
    JEL: C32 C58 G17
    Date: 2013

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