For autoregressive moving average (ARMA) models with infinite variance innovations, quasi-likelihood-based estimators (such as Whittle estimators) suffer from complex asymptotic distributions depending on unknown tail indices. This makes statistical inference for such models difficult. In...

This paper studies the estimation of a semi-strong GARCH(1,1) model when it does not have a stationary solution, where semi-strong means that we do not require the errors to be independent over time. We establish necessary and sufficient conditions for a semi-strong GARCH(1,1) process to have a...

We propose a new method for estimating common factors of multiple time series. One distinctive feature of the new approach is that it is applicable to some nonstationary time series. The unobservable, nonstationary factors are identified by expanding the white noise space step by step, thereby...

The value of the customer has been widely recognized in terms of financial planning and efficient resource allocation including the financial service industry. Previous studies have shown that directly observable information can be used in order to make reasonable predictions of customer...

We propose a new method to determine the cointegration rank in the error correction model of Engle and Granger (1987). To this end, we first estimate the cointegration vectors in terms of a residual-based principal component analysis. Then the cointegration rank, together with the lag order, is...

In order to describe the co-movements in both conditional mean and conditional variance of high dimensional non-stationary time series by dimension reduction, we introduce the conditional heteroscedasticity with factor structure to the error correction model (ECM). The new model is called the...

We propose a new method for estimating common factors of multiple time series. One distinctive feature of the new approach is that it is applicable to some nonstationary time series. The unobservable, nonstationary factors are identified by expanding the white noise space step by step, thereby...

We propose a Bayesian test for nonlinearity of threshold moving average (TMA) models. First, we obtain the marginal posterior densities of all parameters, including the threshold and delay, of the TMA model using Gibbs sampler with the Metropolis-Hastings algorithm. And then, we adopt...