The time series characteristics of postwar US inflation have been found to vary over time. The changes are investigated in a model-based analysis where the time series of inflation is specified by a long memory autoregressive fractionally integrated moving average process with its variance...

Specification procedures for testing the null hypothesis of a Gaussian distribution for the innovations of GARCH models are compared using simulations. More precisely, Cramér–von Mises and Kolmogorov–Smirnov type statistics are computed for empirical processes based on the standardized...

Simulation-based forecasting methods for a non-Gaussian noncausal vector autoregressive (VAR) model are proposed. In noncausal autoregressions the assumption of non-Gaussianity is needed for reasons of identifiability. Unlike in conventional causal autoregressions the prediction problem in...

The Fourier estimator of Malliavin and Mancino depends on both sample size and a so-called cutting frequency. The latter controls the number of Fourier coefficients to be included, and it also determines how the Fourier estimator responds to market microstructure noise. By examining the finite...

An annual sequence of wages in England starting in 1245 is used. It is shown that a standard AK-type growth model with capital externality and stochastic productivity shocks is unable to explain important features of the data. Random returns to scale are then considered. Moderate episodes of...

A flexible importance sampling procedure for the likelihood evaluation of dynamic latent variable models involving mixtures of distributions leading to possibly heavy tailed or multi-modal target densities is provided. The procedure is based upon the efficient importance sampling (EIS) approach...

An EGARCH model in which the conditional distribution is heavy-tailed and skewed is proposed. The properties of the model, including unconditional moments, autocorrelations and the asymptotic distribution of the maximum likelihood estimator, are set out. Evidence for skewness in a conditional...

In recent years, some authors have incorporated the penalized likelihood estimation into designing multivariate control charts under the premise that in practice typically only a small set of variables actually contributes to changes in the process. The advantage of the penalized likelihood...

For the analysis of square contingency tables with ordinal categories, Tahata, Yamamoto and Tomizawa (2009) considered the normal distribution type symmetry model, which may be appropriate if it is reasonable to assume an underlying bivariate normal distribution with equal marginal variances....

Robust multiple linear regression methods are valuable tools when underlying classical assumptions are not completely fulfilled. In this setting, robust methods ensure that the analysis is not significantly disturbed by any outlying observation. However, knowledge of these observations may be...