Autoregressive Process

Autoregressive Process

Any process or model that uses past data to predict future data. Technical analysis, for example, is an autoregressive process. See also: Forecasting.
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Assuming that output gap is autoregressive process, one period ahead forecast may be upward biased in recession and downward biased in boom.
According to Makridakis and Hibon (2000) ARIMA is a combination of 3-parameter model, which consists of Autoregressive process (memory of past events), an integrated process (maintaining and preparing the data fixed and accessible to predict) and moving average (the older the data is the more perfect the prediction will be).
An autoregressive process will only be stable if the parameters are within a certain range: for example, if there is only one autoregressive parameter then is must fall within the interval of 1less than xt less than 1.
To eliminate the autoregressive process of the level shift component, the first difference model only depends on the Bernoulli process: [DELTA] [y.
The co-integration between the prices p is conditioned by the nature of the autoregressive process Zt.
We propose a more robust technique to estimate the effective sample size for the case of an autoregressive process of order 1 (AR1), a suitable hypothesis for many time series in meteorology and climate sciences.
Reference (8) presented modified control limits for maintaining control of a process, when the data were assumed to be dependent and following a second order autoregressive process (AR(2) model).
an autoregressive process where [rho] [less than or equal to] 1.
2]O emissions as the dependent variable and soil water content, soil temperature, autoregressive process, seasonality, and temporal lags of soil water content and soil temperature as potential exploratory variables.
The variable other information also satisfies a first-order autoregressive process (4).
2010b) regime-switching is used to estimate persistence, as a second order autoregressive process, and structural breaks for several inflation indexes using quarterly sample ranging from 1979 to 2010.

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