Shunting freight cars with own power units

In this paper it is shown that shunting freight cars can be simplified significantly if the freight cars have their own power unit. Freight with own power units are extensively discussed in the project FlexCargoRail

A recursive online algorithm for the estimation of time-varying ARCH parameters

In this paper we propose a recursive online algorithm for estimating the parameters of a time-varying ARCH process. The estimation is done by updating the estimator at time point $t-1$ with observations about the time point $t$ to yield an estimator of the parameter at time point $t$. The sampling properties of this estimator are studied in a non-stationary context -- in particular, asymptotic normality and an expression for the bias due to non-stationarity are established. By running two recursive online algorithms in parallel with different step sizes and taking a linear combination of the estimators, the rate of convergence can be improved for parameter curves from H\"{o}lder classes of order between 1 and 2.Comment: Published at http://dx.doi.org/10.3150/07-BEJ5009 in the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm

Statistical inference for time-varying ARCH processes

In this paper the class of ARCH$(\infty)$ models is generalized to the nonstationary class of ARCH$(\infty)$ models with time-varying coefficients. For fixed time points, a stationary approximation is given leading to the notation ``locally stationary ARCH$(\infty)$ process.'' The asymptotic properties of weighted quasi-likelihood estimators of time-varying ARCH$(p)$ processes ($p<\infty$) are studied, including asymptotic normality. In particular, the extra bias due to nonstationarity of the process is investigated. Moreover, a Taylor expansion of the nonstationary ARCH process in terms of stationary processes is given and it is proved that the time-varying ARCH process can be written as a time-varying Volterra series.Comment: Published at http://dx.doi.org/10.1214/009053606000000227 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Graphical Modeling for Multivariate Hawkes Processes with Nonparametric Link Functions

Hawkes (1971) introduced a powerful multivariate point process model of mutually exciting processes to explain causal structure in data. In this paper it is shown that the Granger causality structure of such processes is fully encoded in the corresponding link functions of the model. A new nonparametric estimator of the link functions based on a time-discretized version of the point process is introduced by using an infinite order autoregression. Consistency of the new estimator is derived. The estimator is applied to simulated data and to neural spike train data from the spinal dorsal horn of a rat.Comment: 20 pages, 4 figure

Determinants of credit-less recoveries

This paper aims to shed light on the characteristics and particularly the determinants of credit-less recoveries. After building a dataset and documenting some stylised facts of credit-less recoveries in emerging market economies, this paper uses panel probit models to analyse key determinants of credit-less recoveries. Our main findings are the following. First, our frequency analysis confirms earlier findings that credit-less recoveries are not at all rare events. Moreover, our analysis shows that the frequency of credit-less recoveries doubles after a banking or currency crisis. Second, results from estimated panel probit models suggest that credit-less recoveries are typically preceded by large declines in economic activity and financial stress, in particular if private sector indebtedness is high and the country is reliant on foreign capital inflows. Finally, we find that the predicted probability of a credit-less recovery in central and eastern European EU Member States during the coming years varies across countries, but is relatively high in the Baltic States. JEL Classification: C23, C25, E32, E51, G01Credit-less Recoveries, Financial crises, Panel Probit Models