Periodic Heteroskedastic RegARFIMA models for daily electricity spot prices

In this paper we consider different periodic extensions of regression models with autoregressive fractionally integrated moving average disturbances for the analysis of daily spot prices of electricity. We show that day-of-the-week periodicity and long memory are important determinants for the dynamic modelling of the conditional mean of electricity spot prices. Once an effective description of the conditional mean of spot prices is empirically identified, focus can be directed towards volatility features of the time series. For the older electricity market of Nord Pool in Norway, it is found that a long memory model with periodic coefficients is required to model daily spot prices effectively. Further, strong evidence of conditional heteroskedasticity is found in the mean corrected Nord Pool series. For daily prices at three emerging electricity markets that we consider (APX in The Netherlands, EEX in Germany and Powernext in France) periodicity in the autoregressive coefficients is also stablished, but evidence of long memory is not found and existence of dynamic behaviour in the variance of the spot prices is less pronounced. The novel findings in this paper can have important consequences for the modelling and forecasting of mean and variance functions of spot prices for electricity and associated contingent assetsGARCH, Long Memory

Long Memory Modelling of Inflation with Stochastic Variance and Structural Breaks

We investigate changes in the time series characteristics of postwar U.S. inflation. In a model-based analysis the conditional mean of inflation is specified by a long memory autoregressive fractionally integrated moving average process and the conditional variance is modelled by a stochastic volatility process. We develop a Monte Carlo maximum likelihood method to obtain efficient estimates of the parameters using a monthly data-set of core inflation for which we consider different subsamples of varying size. Based on the new modelling framework and the associated estimation technique, we find remarkable changes in the variance, in the order of integration, in the short memory characteristics and in the volatility of volatility

Time Series Modelling of Daily Tax Revenues

We provide a detailed discussion of the time series modelling of daily tax revenues. The mainfeature of daily tax revenue series is the pattern within calendar months. Standard seasonal timeseries techniques cannot be used since the number of banking days per calendar month varies andbecause there are two levels of seasonality: between months and within months.We start the analysis with a periodic regression model with time varying parameters.This modelis then extended with a component for intra-month seasonality, which is specified as a stochasticcubic spline. State space techniques are used for recursive estimation and evaluation as they allowfor irregular spacing of the time series.The model is recently made operational and used for daily forecasting at the Dutch Ministry ofFinance. For this purpose a front-end for model configuration and data input is implemented withVisual C++, while statistical tools and graphical diagnostics are built around Ox and SsfPack. Wepresent the current model and forecasting results up to December 1999. The model and itsforecasts are evaluated

Outlier detection in GARCH models

We present a new procedure for detecting multiple additive outliers in GARCH(1,1) models at unknown dates. The outlier candidates are the observations with the largest standardized residual. First, a likelihood-ratio based test determines the presence and timing of an outlier. Next, a second test determines the type of additive outlier (volatility or level). The tests are shown to be similar with respect to the GARCH parameters. Their null distribution can be easily approximated from an extreme value distribution, so that computation of "p"-values does not require simulation. The procedure outperforms alternative methods, especially when it comes to determining the date of the outlier. We apply the method to returns of the Dow Jones index, using monthly, weekly, and daily data. The procedure is extended and applied to GARCH models with Student-"t" distributed errors

A Note on the Effect of Seasonal Dummies on the Periodogram Regression

We discuss how prior regression on seasonal dummies leads to singularities in periodogram regression procedures for the detection of long memory. We suggest a modified procedure. We illustrate the problems using monthly inflation data from Hassler and Wolters (1995)

Structural basis of template-boundary definition in Tetrahymena telomerase.

Telomerase is required to maintain repetitive G-rich telomeric DNA sequences at chromosome ends. To do so, the telomerase reverse transcriptase (TERT) subunit reiteratively uses a small region of the integral telomerase RNA (TER) as a template. An essential feature of telomerase catalysis is the strict definition of the template boundary to determine the precise TER nucleotides to be reverse transcribed by TERT. We report the 3-Å crystal structure of the Tetrahymena TERT RNA-binding domain (tTRBD) bound to the template boundary element (TBE) of TER. tTRBD is wedged into the base of the TBE RNA stem-loop, and each of the flanking RNA strands wraps around opposite sides of the protein domain. The structure illustrates how the tTRBD establishes the template boundary by positioning the TBE at the correct distance from the TERT active site to prohibit copying of nontemplate nucleotides

Periodic Unobserved Cycles in Seasonal Time Series with an Application to US Unemployment

This paper discusses identification, specification, estimation and forecasting for a general class of periodic unobserved components time series models with stochastic trend, seasonal and cycle components. Convenient state space formulations are introduced for exact maximum likelihood estimation, component estimation and forecasting. Identification issues are considered and a novel periodic version of the stochastic cycle component is presented. In the empirical illustration, the model is applied to postwar monthly US unemployment series and we discover a significantly periodic cycle. Furthermore, a comparison is made between the performance of the periodic unobserved components time series model and a periodic seasonal autoregressive integrated moving average model. Moreover, we introduce a new method to estimate the latter model

Testate amoebae as proxy for water level changes in a brackish tidal marsh

Few studies have examined testate amoebae assemblages of estuarine tidal marshes. This study investigates the possibility of using soil testate amoebae assemblages of a brackish tidal marsh (Scheldt estuary, Belgium) as a proxy for water level changes. On the marsh surface an elevation gradient is sampled to be analyzed for testate amoebae assemblages and sediment characteristics. Further, vegetation, flooding frequency and soil conductivity have been taken into account to explain the testate amoebae species variation. The data reveal that testate amoebae are not able to establish assemblages at the brackish tidal marsh part with flooding frequencies equal to or higher than 36.5%. Further, two separate testate amoebae zones are distinguished based on cluster analysis. The lower zone’s testate amoebae species composition is influenced by the flooding frequency (~ elevation) and particle size, while the species variability in the higher zone is related to the organic content of the soil and particle size. These observations suggest that the ecological meaning of elevation shifts over its range on the brackish tidal marsh. Testate amoeba assemblages in such a brackish habitat show thus a vertical zonation (RMSEP: 0.19 m) that is comparable to the vertical zonation of testate amoebae and other protists on freshwater tidal marshes and salt marshes