Functional data analysis (FDA) has become an important area of statistics research in the recent decade, yet a smooth simultaneous confidence corridor (SCC) does not exist in the literature for the mean function of sparse functional data. SCC is a powerful tool for making statistical inference...

This paper proposes a nonparametric test of Granger causality in quantile. Zheng (1998, <italic>Econometric Theory</italic> 14, 123–138) studied the idea to reduce the problem of testing a quantile restriction to a problem of testing a particular type of mean restriction in independent data. We extend...

In this paper bootstrap confidence bands are constructed for nonparametric quantile estimates of regression functions, where resampling is done from a suitably estimated empirical distribution function (edf) for residuals. It is known that the approximation error for the confidence band by the...

Based on the Lee-Carter (LC) model, the benchmark in population forecasting, a variety of extensions and modifications are proposed in this paper. We investigate one of the extensions, the Hyndman-Ullah (HU) method and apply it to Asian demographic data sets: China, Japan and Taiwan. It combines...

High‐dimensional non‐stationary time series, which reveal both complex trends and stochastic behaviour, occur in many scientific fields, e.g. macroeconomics, finance, neuroeconomics, etc. To model these, we propose a generalized dynamic semi‐parametric factor model with a two‐step...

Modern econometrics requires implementation of highly specialized software. In contrast to mathematical arguments used in implementing new econometric techniques the corresponding software algorithms require specific platforms. The specialization of hardware and software, in fact, seriously...

It is common practice to identify the number and sources of shocks that move implied volatilities across space and time by applying Principal Components Analysis (PCA) to pooled covariance matrices of changes in implied volatilities. This approach, however, is likely to result in a loss of...

Stochastic Volatility (SV) models are widely used in financial applications. To decide whether standard parametric restrictions are justified for a given dataset, a statistical test is required. In this paper, we develop such a test based on the linear state space representation. We provide a...

Motivated by the recurrent Neural Networks, this paper proposes a recurrent Support Vector Regression (SVR) procedure to forecast nonlinear ARMA model based simulated data and real data of financial returns. The forecasting ability of the recurrent SVR is compared with three competing methods,...