COINTEGRATION IN FUNCTIONAL AUTOREGRESSIVE PROCESSES

Gaussian Processes for Functional-Coefficient Autoregressive Models

This work is concerned with nonlinear time series models and, in particular, with nonparametric models for the dynamics of the mean of the time series. We build on the functional-coefficient autoregressive (FAR) model of Chen and Tsay (1993) which is a generalization of the autoregressive (AR) model where the coefficients are varying and are given by functions of the lagged values of the series...

Modelling of cointegration in the vector autoregressive model

A survey is given of some results obtained for the cointegrated VAR. The Granger representation theorem is discussed and the notions of cointegration and common trends Ž . are defined. The statistical model for cointegrated I 1 variables is defined, and it is shown how hypotheses on the cointegrating relations can be estimated under suitable identification conditions. The asymptotic theory is b...

Multistep Prediction in Autoregressive Processes

In this paper, two competing types of multistep predictors, i+e+, plug-in and direct predictors, are considered in autoregressive ~AR! processes+When a working model AR~k! is used for the h-step prediction with h . 1, the plug-in predictor is obtained from repeatedly using the fitted ~by least squares! AR~k! model with an unknown future value replaced by their own forecasts, and the direct pred...

Time - series Econometrics : Cointegration and Autoregressive Conditional Heteroskedasticity

An Autoregressive Distributed Lag Modelling Approach to Cointegration Analysis¤

This paper examines the use of autoregressive distributed lag (ARDL) models for the analysis of long-run relations when the underlying variables are I(1). It shows that after appropriate augmentation of the order of the ARDL model, the OLS estimators of the short-run parameters are p T -consistent with the asymptotically singular covariance matrix, and the ARDL-based estimators of the long-run ...