This paper provides a review of some recent theoretical results for time series models with GARCH errors, and is directed towards practitioners. Starting with the simple ARCH model and proceeding to the GARCH model, some results for stationary and nonstationary ARMA-GARCH are summarized. Various...

Least squares (LS) and maximum likelihood (ML) estimation are considered for unit root processes with GARCH (1, 1) errors. The asymptotic distributions of LS and ML estimators are derived under the condition alpha + beta 1. The former has the usual unit root distribution and the latter is a...

This paper examines stationary and nonstationary time series by formally testing for the presence of unit roots and seasonal unit roots prior to estimation, model selection and forecasting. Various Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) models are estimated over the period...

This paper investigates some structural properties of a family of GARCH processes. A simple sufficient condition for the existence of the αδ-order stationary solution of the processes is derived, where α ∈ (0, 1] and δ 0. The solution is strictly stationary and ergodic, and the causal...

This paper studies the issue of designing an optimal organizational form: design for sub-units’ task allocation, decision-making structure, and incentive schemes for organizational members. Depending on the way tasks are allocated between the sub-units, and whether decision-making is...

This paper considers adaptive estimation in nonstationary autoregressive moving average models with the noise sequence satisfying a generalised autoregressive conditional heteroscedastic process. The locally asymptotic quadratic form of the log-likelihood ratio for the model is obtained. It is...

This article develops parametric and semi-parametric, maximum likelihood estimation methods of the proportional hazard model for the case when durations are grouped and heterogeneity is not fully measured. This article also extends Ryu’s (1994b) specification tests for the proportional hazard...