Historical records for rivers in Fars Province are inadequate in comparison with the design period of hydraulic structures. In this study, time series techniques are applied to the records of three Iranian rivers in the Fars Province in order to generate forecast values of the mean monthly river flows. The autoregressive models (AR), moving average models (MA) and autoregressive moving ave...

In this paper we examine the forecast accuracy of four univariate time series models for 47 macroeconomic variables of the G7 economies. The models considered are the linear autoregressive model, the smooth transition autoregressive model, and two neural network models. The two neural network models are different because they are specified using two different techniques. Forecast accuracy is as...

We study situations in which autoregressive models are estimated on time series that contain switches in the data generating parameters and these switches are not accounted for. The geometry of this estimation problem causes estimated vector autoregressive models to display a unit eigenvalue, and the sum of the estimated autoregressive parameters of ARMA and GARCH models to be close to one. Thi...

This paper is concerned with univariate noncausal autoregressive models and their potential usefulness in economic applications. We argue that noncausal autoregressive models are especially well suited for modeling expectations. Unlike conventional causal autoregressive models, they explicitly show how the considered economic variable is a¤ected by expectations and how expectations are formed. ...

This paper reviews the analysis of the threshold autoregressive, smooth threshold autoregressive, and Markov switching autoregressive models from the Bayesian perspective. For each model we start by describing a baseline model and discussing possible extensions and applications. Then we review the choice of prior, inference, tests against the linear hypothesis, and conclude with models selectio...

We report preliminary results of an eeort to use variants of the Hidden Markov Models developed by speech researchers to characterize persistence and recurrence of atmospheric circulation patterns in a 36 year record of Northern Hemisphere 700-mb geopotential heights. Using a cross validation scheme, we t autoregressive hidden Markov models (ARHMMs) with a range of complexities , varying the au...

the purpose of this study is to determine spatial dependency pattern of systematic risk of dry land wheat production in iran, using spatial autoregressive models. to this end, spatial weighted contiguity matrix was constructed based on the delaunay triangularization method, and correlation coefficient among these neighbors were estimated using spatial autoregressive models. in addition, the rol...

In this paper we propose a class of time-domain models for analyzing possibly nonstationary time series. This class of models is formed as a mixture of time series models, whose mixing weights are a function of time. We consider specifically mixtures of autoregressive models with a common but unknown lag. To make the methodology work we show that it is necessary to first partition the data into...

Recently, the image and video coding community has witnessed several proposals to improve coding efficiency by exploiting perceptual redundancy of texture. Most of these approaches are based on segmentation and non-parametric texture models popular in the computer graphics domain. Although not a generic model for everything we might call texture, the simple (and parametric) autoregressive model...