We review the past 25 years of time series research that has been published in journals managed by the International Institute of Forecasters (Journal of Forecasting 1982-1985; International Journal of Forecasting 1985-2005). During this period, over one third of all papers published in these...

In this paper we investigate the forecasting performance of the non-linear time series SETAR model by using Canadian GDP data from 1965 to 2000. Besides the with-in-sample fit, the forecasting performance of a standard linear ARIMA model for the same sample has also been generated for...

An important limitation in order to specify and estimate a macroeconomic model that describes the Chilean economy resides in using variables with sufficient number of observations that allow for a reliable econometric estimation. Among these variables, th

Technological developments and the global economic crisis are two types of developments that have affected the commercial airline industry in the last decade. This paper investigates time series analysis of the airline industry. The research has been conducted and is being presented, in a number...

We show that the use of prior information derived from former empirical findings and/or subject matter theory regarding the lag structure of the observable variables together with an AR process for the error terms can produce univariate and single equation models that are intuitively appealing,...

We study how the round-off (or discretization) error changes the statistical properties of a Gaussian long memory process. We show that the autocovariance and the spectral density of the discretized process are asymptotically rescaled by a factor smaller than one, and we compute exactly this...

VARMA models can be parameterized by using the echelon form, which is characterized by the Kronecker indices. Three different methods for estimating the Kronecker indices of echelon-form VARMA models are discussed and compared. The three methods are expected to work even for non-stationary...

This Eviews Program is a simple program that help you to select the best ARMA model based on Schwartz or Alaike criterion. You just enter max AR and MA order and finally the program selects the best model

Standard estimation of ARMA models in which the AR and MA roots nearly cancel, so that individual coefficients are only weakly identified, often produces inferential ranges for individual coefficients that give a spurious appearance of accuracy. We remedy this problem with a model that uses a...