agriculture as one of the major economic sectors of iran, has an important role in gross domestic production by providing about 14% of gdp. this study attempts to forecast the value of the agriculture gdp using periodic autoregressive model (par), as the new seasonal time series techniques. to address this aim, the quarterly data were collected from march 1988 to july 1989. the collected data w...

Periodic models for seasonal data allow the parameters of the model to vary across the different seasons. This paper uses the components of UK consumption to see whether the periodic autoregressive (PAR) model yields more accurate forecasts than non-periodic models, such as the airline model of Box and Jenkins (1970), and autoregressive models that pre-test for (seasonal) unit roots. We analyse...

An overview of model building with periodic autoregression (PAR) models is given emphasizing the three stages of model development: identification, estimation and diagnostic checking. New results on the distribution of residual autocorrelations and suitable diagnostic checks are derived. The validity of these checks is demonstrated by simulation. The methodology discussed is illustrated with an...

The objective of this study was to model seasonal behavior of broiler price in Iran that can be used to forecast the monthly broiler prices. In this context, the periodic autoregressive (PAR), the seasonal integrated models, and the Box-Jenkins (SARIMA) models were used as the primary nominates for the forecasting model. It was shown that the PAR (q) model could not be considered as an appropri...

This paper examines the effect of X-11 seasonal adjustment on periodic autoregressive processes, using both analytic techniques and simulation. Analytical results show that adjustment reduces (but does not eliminate) periodicity in the coefficients of a stationary PAR(1) process, and it generally moves the coefficients towards unity. A nonstationary periodically integrated process is converted ...

Abstract: In this paper, the performance of Auto-Regressive (AR) and Periodic AutoRegressive (PAR) algorithms when used to predict cyclostationary signals is analyzed and evaluated. Both analytical and computer simulation results indicate that when predicting cyclostationary signals, the PAR predictor significantly outperforms the AR predictor at the expense of higher computational complexity. ...

This work deals with the limiting distribution of the least squares estimators of the coefficients ar of an explosive periodic autoregressive of order 1 (PAR(1)) time series Xr = arXr−1+ur when the innovation {uk} is strongly mixing. More precisely {ar} is a periodic sequence of real numbers with period P > 0 and such that ∏P r=1 |ar| > 1. The time series {ur} is periodically distributed with t...

This paper proposes a stochastic volatility model (PAR-SV ) in which the log-volatility follows a …rst-order periodic autoregression. This model aims at representing time series with volatility displaying a stochastic periodic dynamic structure, and may then be seen as an alternative to the familiar periodic GARCH process. The probabilistic structure of the proposed PAR-SV model such as periodi...

We propose an autoregressive conditional duration (ACD) model with periodic time-varying parameters and multiplicative error form. name this (PACD). First, we study the stability properties moment structures of it. Second, estimate parameters, using (profile two-stage) Gamma quasi-maximum likelihood estimates (QMLEs), asymptotic which are examined under general regularity conditions. Our estima...