nep-ets New Economics Papers
on Econometric Time Series
Issue of 2015‒08‒30
eight papers chosen by
Yong Yin
SUNY at Buffalo

  1. Estimation of Fractionally Integrated Panels with Fixed Effects and Cross-Section Dependence By Yunus Emre Ergemen; Carlos Velasco
  2. Detecting intraday financial market states using temporal clustering By Dieter Hendricks; Tim Gebbie; Diane Wilcox
  3. Do We Need Ultra-High Frequency Data to Forecast Variances? By Georgiana-Denisa Banulescu; Bertrand Candelon; Christophe Hurlin; Sébastien Laurent
  4. Testing for Linearity in Regressions with I(1) Processes By Yoichi Arai;
  5. The multivariate Beveridge--Nelson decomposition with I(1) and I(2) series By Murasawa, Yasutomo
  6. Covariate-augmented unit root tests with mixed-frequency data By Cláudia Duarte
  7. Multivariate Dynamic Copula Models: Parameter Estimation and Forecast Evaluation By Aepli, Matthias D.; Frauendorfer, Karl; Fuess, Roland; Paraschiv, Florentina
  8. Autocorrelation robust inference using the Daniell kernel with fixed bandwidth By Javier Hualde; Fabrizio Iacone

  1. By: Yunus Emre Ergemen (Aarhus University and CREATES); Carlos Velasco (Universidad Carlos III de Madrid)
    Abstract: We consider large N, T panel data models with fixed effects, common factors allowing cross-section dependence, and persistent data and shocks, which are assumed fractionally integrated. In a basic setup, the main interest is on the fractional parameter of the idiosyncratic component, which is estimated in first differences after factor removal by projection on the cross-section average. The pooled conditional-sum-of-squares estimate is root-NT consistent but the normal asymptotic distribution might not be centered, requiring the time series dimension to grow faster than the cross-section size for correction. Generalizing the basic setup to include covariates and heterogeneous parameters, we propose individual and common-correlation estimates for the slope parameters, while error memory parameters are estimated from regression residuals. The two parameter estimates are root-T consistent and asymptotically normal and mutually uncorrelated, irrespective of possible cointegration among idiosyncratic components. A study of small-sample performance and an empirical application to realized volatility persistence are included.
    Keywords: Fractional cointegration, factor models, long memory, realized volatility
    JEL: C22 C23
    Date: 2015–08–17
  2. By: Dieter Hendricks; Tim Gebbie; Diane Wilcox
    Abstract: We propose the application of a high-speed maximum likelihood clustering algorithm to detect temporal financial market states, using correlation matrices estimated from intraday market microstructure features. We first determine the ex-ante intraday temporal cluster configurations to identify market states, and then study the identified temporal state features to extract state signature vectors which enable online state detection. The state signature vectors serve as low-dimensional state descriptors which can be used in learning algorithms for optimal planning in the high-frequency trading domain. We present a feasible scheme for real-time intraday state detection from streaming market data feeds. This study identifies an interesting hierarchy of system behaviour which motivates the need for time-scale-specific state space reduction for participating agents.
    Date: 2015–08
  3. By: Georgiana-Denisa Banulescu (Maastricht University - univ. Maastricht, LEO - Laboratoire d'économie d'Orleans - UO - Université d'Orléans - CNRS); Bertrand Candelon (Maastricht University - univ. Maastricht); Christophe Hurlin (LEO - Laboratoire d'économie d'Orleans - UO - Université d'Orléans - CNRS); Sébastien Laurent (AMU IAE - Institut d'Administration des Entreprises (IAE) - Aix-en-Provence - AMU - Aix-Marseille Université)
    Abstract: In this paper we study various MIDAS models in which the future daily variance is directly related to past observations of intraday predictors. Our goal is to determine if there exists an optimal sampling frequency in terms of volatility prediction. Via Monte Carlo simulations we show that in a world without microstructure noise, the best model is the one using the highest available frequency for the predictors. However, in the presence of microstructure noise, the use of ultra high-frequency predictors may be problematic, leading to poor volatility forecasts. In the application, we consider two highly liquid assets (i.e., Microsoft and S&P 500). We show that, when using raw intraday squared log-returns for the explanatory variable, there is a "high-frequency wall" or frequency limit above which MIDAS-RV forecasts deteriorate. We also show that an improvement can be obtained when using intraday squared log-returns sampled at a higher frequency, provided they are pre-filtered to account for the presence of jumps, intraday periodicity and/or microstructure noise. Finally, we compare the MIDAS model to other competing variance models including GARCH, GAS, HAR-RV and HAR-RV-J models. We find that the MIDAS model provides equivalent or even better variance forecasts than these models, when it is applied on filtered data.
    Date: 2014–10–26
  4. By: Yoichi Arai (GRIPS);
    Abstract: We propose a generalized version of the RESET test for linearity in regressions with I(1) processes against various nonlinear alternatives and no cointegration. The proposed test statistic for linearity is given by the Wald statistic and its limiting distribution under the null hypothesis is shown to be a X^2 distribution with a "leads and lags" estimation technique. We show that the test is consistent against a class of nonlinear alternatives and no cointegration. Finite-sample simulations show that the empirical size is close to the nominal one and the test succeeds in detecting both nonlinearity and no cointegration.
    Date: 2015–08
  5. By: Murasawa, Yasutomo
    Abstract: The consumption Euler equation implies that the output growth rate and the real interest rate are of the same order of integration; i.e., if the real interest rate is I(1), then so is the output growth rate and hence log output is I(2). To estimate the natural rates and gaps of macroeconomic variables jointly, this paper develops the multivariate Beveridge--Nelson decomposition with I(1) and I(2) series. The paper applies the method to Japanese data during 1980Q1--2013Q3 to estimate the natural rates and gaps of output, inflation, interest, and unemployment jointly.
    Keywords: gap; natural rate; trend--cycle decomposition; unit root
    JEL: C32 C82 E32
    Date: 2015–08–28
  6. By: Cláudia Duarte
    Abstract: Unit root tests typically suer from low power in small samples, which results in not rejecting the null hypothesis as often as they should. This paper tries to tackle this issue by assessing whether it is possible to improve the power performance of covariate-augmented unit root tests, namely the ADF family of tests, by exploiting mixed-frequency data. We use the mixed data sampling (MIDAS) approach to deal with mixed-frequency data. The results from a Monte Carlo exercise indicate that mixed-frequency tests have better power performance than low-frequency tests. The gains from exploiting mixed-frequency data are greater for near-integrated variables. An empirical illustration using the US unemployment rate is presented.
    JEL: C12 C15 C22
    Date: 2015
  7. By: Aepli, Matthias D.; Frauendorfer, Karl; Fuess, Roland; Paraschiv, Florentina
    Abstract: This paper introduces multivariate dynamic copula models to account for the time-varying dependence structure in asset portfolios. We firstly enhance the flexibility of this structure by modeling regimes with multivariate mixture copulas. In our second approach, we derive dynamic elliptical copulas by applying the dynamic conditional correlation model (DCC) to multivariate elliptical copulas. The best-ranked copulas according to both in-sample fit and out-of-sample forecast performance indicate the importance of accounting for time-variation. The superiority of multivariate dynamic Clayton and Student-t models further highlight that positive tail dependence as well as the capability of capturing asymmetries in the dependence structure are crucial features of a well-fitting model for an equity portfolio.
    Keywords: Multivariate dynamic copulas, regime-switching copulas, dynamic conditional correlation (DCC) model, forecast performance, tail dependence.
    JEL: C32 C51 C53
    Date: 2015–07
  8. By: Javier Hualde; Fabrizio Iacone
    Abstract: We consider alternative asymptotics for frequency domain estimates of the long run variance, in which the bandwidth is kept fixed. For a weakly dependent process, this does not yield a consistent estimateof the long run variance, but the standardized mean has t limit distribution, which, for any given bandwidth, appears to be more precise than the traditional Gaussian limit. In presence of fractionally integrated data, the limit distribution of the estimate is not standard, and we derive critical values for the standardized mean for various bandwidths. Again, we find that this asymptotic result provides a better approximation than other proposals like the Memory Autocorrelation Consistent (MAC) estimate. In multivariate set up, fixed bandwidth asymptotics may be also used to provide a characterization to the limit distribution of estimates of cointegrating parameter which differs substantially from the conventional Narrow Band asymptotics.
    Keywords: long run variance estimation, long memory, large-m and fixed-masymptotic theory
    JEL: C32
    Date: 2015–08

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