nep-ets New Economics Papers
on Econometric Time Series
Issue of 2015‒05‒22
five papers chosen by
Yong Yin
SUNY at Buffalo
  1. Testing for First Order Serial Correlation in Temporally Aggregated Regression Models By Helson C. Braga; William G. Tyler
  2. Trend, Seasonality and Seasonal Adjustment By A. C. Harvey; Pedro L. Valls Pereira
  3. Forecasting Euro Area Macroeconomic Variables with Bayesian Adaptive Elastic Net By Sandra Stankiewicz
  4. FloGARCH : Realizing long memory and asymmetries in returns volatility By Harry Vander Elst
  5. The Impact of Jumps and Leverage in Forecasting Co-Volatility By Manabu Asai; Michael McAleer
  1. By: Helson C. Braga; William G. Tyler
    Abstract: Thls paper shows that the LM statistic for testing first order serial correlation in regression models can be computed using the Kalman Filter. It is shown tha.t when there are missing observations, the LM statistic for this tesi is equivalent to the tesi statistic derived by Robinson (1985) using the likelihood conditional on the observation times. The Kalman Filter approach is preferable because the test statistic for first order serial correlation in t.emporally aggregated regression models can be obta.ined as an extension of the previous case..
    Date: 2015–01
    URL: http://d.repec.org/n?u=RePEc:ipe:ipetds:0014&r=ets
  2. By: A. C. Harvey; Pedro L. Valls Pereira
    Date: 2015–01
    URL: http://d.repec.org/n?u=RePEc:ipe:ipetds:0019&r=ets
  3. By: Sandra Stankiewicz (Department of Economics, University of Konstanz, Germany)
    Abstract: I use the adaptive elastic net in a Bayesian framework and test its forecasting performance against lasso, adaptive lasso and elastic net (all used in a Bayesian framework) in a series of simulations, as well as in an empirical exercise for macroeconomic Euro area data. The results suggest that elastic net is the best model among the four Bayesian methods considered. Adaptive lasso, on the other hand, shows the worst forecasting performance. Lasso is generally better then adaptive lasso, but worse than adaptive elastic net. The differences in the performance of these models become especially large when the number of regressors grows considerably relative to the number of available observations. The results point to the fact that the ridge regression component in the elastic net is responsible for its improvement in forecasting performance over lasso. The adaptive shrinkage in some of the models does not seem to play a major role, and may even lead to a deterioration of the performance.
    Keywords: Elastic net, Lasso, Bayesian, Forecasting
    JEL: C11 C22 C53
    Date: 2015–05–13
    URL: http://d.repec.org/n?u=RePEc:knz:dpteco:1512&r=ets
  4. By: Harry Vander Elst (Université libre de Bruxelles)
    Abstract: We introduce the class of FloGARCH models in this paper. FloGARCH models provide a parsimonious joint model for low frequency returns and realized measures and are sufficiently flexible to capture long memory as well as asymmetries related to leverage effects. We analyze the performances of the models in a realistic numerical study and on the basis of a data set composed of 65 equities. Using more than 10 years of high-frequency transactions, we document significant statistical gains related to the FloGARCH models in terms of in-sample fit, out-of-sample fit and forecasting accuracy compared to classical and Realized GARCH models.
    Keywords: Realized GARCH models, high-frequency data, long memory, realized measures.
    JEL: C22 C53 C58 G17
    Date: 2015–04
    URL: http://d.repec.org/n?u=RePEc:nbb:reswpp:201504-280&r=ets
  5. By: Manabu Asai (Faculty of Economics, Soka University); Michael McAleer (Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam and Tinbergen Institute, The Netherlands, Department of Quantitative Economics, Complutense University of Madrid, and Institute of Economic Research, Kyoto University.)
    Abstract: The paper investigates the impact of jumps in forecasting co-volatility, accommodating leverage effects. We modify the jump-robust two time scale covariance estimator of Boudt and Zhang (2013) such that the estimated matrix is positive definite. Using this approach we can disentangle the estimates of the integrated co-volatility matrix and jump variations from the quadratic covariation matrix. Empirical results for three stocks traded on the New York Stock Exchange indicate that the co-jumps of two assets have a significant impact on future co-volatility, but that the impact is negligible for forecasting weekly and monthly horizons.
    Keywords: Co-Volatility; Forecasting; Jump; Leverage Effects; Realized Covariance; Threshold Estimation.
    JEL: C32 C53 C58 G17
    Date: 2015–02
    URL: http://d.repec.org/n?u=RePEc:ucm:doicae:1502&r=ets
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