The Power of the Tests of Robinson (1994) in the Context of Fractionally Integrated Moving Average Models
نویسنده
چکیده
We examine in this article the power of the tests of Robinson (1994) for testing I(d) statistical models in the presence of moving average (MA) disturbances. The results show that the tests behave relatively well if we correctly assume that the disturbances are MA. However, assuming white noise or autoregressive disturbances, the power of the tests against one-sided alternatives is very low. JEL Classification: C12; C15.
منابع مشابه
Statistical trend analysis and forecast modeling of air pollutants
The study provides a statistical trend analysis of different air pollutants using Mann-Kendall and Sen’s slope estimator approach on past pollutants statistics from air quality index station of Varanasi, India. Further, using autoregressive integrated moving average model, future values of air pollutant levels are predicted. Carbon monoxide, nitrogen dioxide, sulphur dioxide, particu...
متن کاملDierential Geometry of Autoregressive Fractionally Integrated Moving Average Models
The di erential geometry of autoregressive fractionally integrated moving average processes is developed. Properties of Toeplitz forms associated with the spectral density functions of these long memory processes are used to compute the geometric quantities. The role of these geometric quantities on the asymptotic bias of the maximum likelihood estimates of the model parameters and on the Bartl...
متن کاملUsing a Fuzzy Auto Regressive Integrated Moving Average Model for Exchange Rate Forecasting
Forecasting models have wide applications in decision making. In the real world, rapid changes normally take place in different areas, specifically in financial markets. Collecting the required data is a main problem for forecasters in such unstable environments. Forecasting methods such as Auto Regressive Integrated Moving Average (ARIMA) models and also Artificial Neural Networks (ANNs) need ...
متن کاملUsing a Fuzzy Auto Regressive Integrated Moving Average Model for Exchange Rate Forecasting
Forecasting models have wide applications in decision making. In the real world, rapid changes normally take place in different areas, specifically in financial markets. Collecting the required data is a main problem for forecasters in such unstable environments. Forecasting methods such as Auto Regressive Integrated Moving Average (ARIMA) models and also Artificial Neural Networks (ANNs) need ...
متن کاملMODELING THE STOCHASTIC BEHAVIOR OF THE FARS RIVERS
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...
متن کامل