The Consequences of Seasonal Adjustment for Periodic Autoregressive Processes

Seasonality and Forecasting of Monthly Broiler Price in Iran

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...

Testing for Seasonal Unit Roots in Periodic Integrated Autoregressive Processes

Periodic Autoregressive Conditional

Most high-frequency asset returns exhibit seasonal volatility patterns. This article proposes a new class of models featuring periodicity in conditional heteroscedasticity explicitly designed to capture the repetitive seasonal time variation in the second-order moments. This new class of periodic autoregressive conditional heteroscedasticity, or P-ARCH, models is directly related to the class o...

Testing and modelling autoregressive conditional heteroskedasticity of streamflow processes

Abstract. Conventional streamflow models operate under the assumption of constant variance or season-dependent variances (e.g. ARMA (AutoRegressive Moving Average) models for deseasonalized streamflow series and PARMA (Periodic AutoRegressive Moving Average) models for seasonal streamflow series). However, with McLeod-Li test and Engle’s Lagrange Multiplier test, clear evidences are found for t...

Periodic Cointegration Analysis on the Relationship between the GNP Sectors of the Iran’s Economy

T his paper analyses the relation between GNP sectors of Iran’s economy. The different sectors of the economy directly or indirectly affect each other and can complement or follow each other. Using a number of empirical tests, the paper finds evidence of seasonal or periodic integration in the underlying data. This means that the conventional cointegration tests may not be robust and in re...