В статье выполнена статистическая проверка эмпирических распределений макроэкономических индикаторов РФ (среднемесячная номинальная начисленная зарплата,...

In this paper we propose a test statistic to discriminate between models with finite variance and models with infinite variance. The test statistic is the ratio of the sample standard deviation and the sample interquartile range. Both asymptotic and finite sample properties of the test statistic...

Conference Paper

Chapter 1 is concerned with confidence interval construction for the mean of a long-range dependent time series. It is well known that the moving block bootstrap method produces an inconsistent estimator of thedistribution of the normalized sample mean when its limiting distribution is not...

This thesis mainly builds on the Variance Gamma (VG) model for financial assets over time of Madan & Seneta (1990) and Madan, Carr & Chang (1998), although the model based on the t distribution championed in Heyde & Leonenko (2005) is also given attention. The primary contribution of the thesis...

Several features may be present in rainfall data, and sophisticated time series procedures are needed for the analysis. These features are that of seasonality, long range dependency of observations and time trend as observed in the climatological series. This paper therefore considered the...

Multifractal processes reproduce some of the stylised features observed in financial time series, namely heavy tails found in asset returns distributions, and long-memory found in volatility. Multifractal scaling cannot be assumed, it should be established; however, this is not a straightforward...

We derive the limiting null distribution of the robust CUSUM-M test and the recursive CUSUM-M test for structural change of the coefficients of a linear regression model with long-memory disturbances. It turns out that the asymptotic null distribution of the CUSUM-M statistic is a fractional...

Nonparametric regression with spatial, or spatio-temporal, data is considered. The conditional mean of a dependent variable, given explanatory ones, is a nonparametric function, while the conditional covariance reflects spatial correlation. Conditional heteroscedasticity is also allowed, as well...