Econometric Time Series
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Econometric Time Series2015-05-22Yong YinTesting for First Order Serial Correlation in Temporally Aggregated Regression Models
http://d.repec.org/n?u=RePEc:ipe:ipetds:0014&r=ets
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..Helson C. Braga, William G. Tyler2015-01Trend, Seasonality and Seasonal Adjustment
http://d.repec.org/n?u=RePEc:ipe:ipetds:0019&r=ets
A. C. Harvey, Pedro L. Valls Pereira2015-01Forecasting Euro Area Macroeconomic Variables with Bayesian Adaptive Elastic Net
http://d.repec.org/n?u=RePEc:knz:dpteco:1512&r=ets
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.Sandra Stankiewicz2015-05-13Elastic net, Lasso, Bayesian, ForecastingFloGARCH : Realizing long memory and asymmetries in returns volatility
http://d.repec.org/n?u=RePEc:nbb:reswpp:201504-280&r=ets
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.Harry Vander Elst2015-04Realized GARCH models, high-frequency data, long memory, realized measures.The Impact of Jumps and Leverage in Forecasting Co-Volatility
http://d.repec.org/n?u=RePEc:ucm:doicae:1502&r=ets
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.Manabu Asai, Michael McAleer2015-02Co-Volatility; Forecasting; Jump; Leverage Effects; Realized Covariance; Threshold Estimation.