Autoregressive

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Autoregressive

Using past data or variable of interest to predict future values of the same variable.

Autoregressive

Anything that uses past data to predict future data. Technical analysis, for example, is by its nature autoregressive. See also: Forecasting.
References in periodicals archive ?
Thus, this combination characterizes the model defined by the literature as Autoregressive Moving Average Model (ARMA).
Several time series models have been used, such as autoregressive model (AR) or autoregressive moving average model (ARMA).
We find use of random walk or RW (like in Atkeson and Ohanian (2001)); autoregressive moving average model of integration of order 1 or ARIMA (like in Narayan and Cicarelli (1982), Claus and Claus (2002), Benkovskis (2008) and Adebiyi et al (2014)); and autoregressive model of order 1 or (AR(1) (like in Faust and Wright (2013)) as benchmark models.
Autoregressive Moving Average Model of order (p, q), denoted by ARMA (p, q)
He describes several nonlinear system identification methods, but focuses particularly on nonlinear autoregressive moving average model with exogenous inputs (NARMAX) methods.
The autoregressive moving average model with order p and q (ARMA(p, q)) is given as below:
Some of these procedures, which have their methodological based in PA, help tackle the specification of a particular kind of vector autoregressive moving average model, such as the transfer function (TF) model [Box and Jenkins, 1976].
which is an autoregressive moving average model with exogenous variables (an ARMAX model).
Killingsworth estimates the effect of EPEV on the award rates of women and men by using an autoregressive moving average model based on quarterly data for 15 years, ending in 1982.
Time series models consist of autoregressive, moving average, autoregressive moving average models.
He uses econometric examples involving practical policy issues in order to discuss production function and regression methods; univariate time series analysis; bivariate time series analysis including stochastic diffusion and cointegration; utility theory and empirical implications; vector models for multivariate problems; simultaneous equation models; limited dependent variable models; dynamic optimization and empirical analysis of consumer behavior; single, double, and maximum entropy bootstrap and inference; generalized least squares, vector autoregressive moving average models, and estimating functions; and nonlinear models and projection pursuit regression.
Testing for Unit Roots in Autoregressive Moving Average Models of Unknown Order.

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