Autoregressive


Also found in: Acronyms.

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 ?
They apply an autoregressive distributed lag model including daily data for the period November 2007 to july 2009, i.e.
Let us now complete the model specification by proposing an appropriate exogenous dynamics for the dynamic frailty process, called (first-order) autoregressive gamma process (ARG).
Thus, the autoregressive model, estimated for the variation of the IPCA, via regression, is: [[??].sub.t] = 0.19 + 0.60 [y.sub.t-1]
where [X.sub.t] = data series, [a.sub.t] = random error (with mean zero and variance [[sigma].sup.2]), B = backward shift operator, [phi] = coefficient non-seasonal autoregressive, [theta] = coefficient non-seasonal moving average, [PHI] = coefficient seasonal autoregressive, [THETA] = coefficient seasonal moving average, [[DELTA].sup.d] = difference operator, with d order of differencing, and [[DELTA].sup.D.sub.s] = seasonal difference operator, with D seasonal order of differencing and s length of the seasonal period.
From this Figure, we observe that the streamflow series have a periodic behavior in the mean and variance and in this case, general periodic autoregressive models are usually assumed in the analysis of the time series data (Modal & Wasimi, 2006).
First, although we used a fully cross-lagged panel design and controlled for autoregressive effects, the nonexperimental study design means that we cannot derive conclusions about the causal relationships between loneliness and GPIU.
Therefore, the adjustment quality evaluators highlight the superiority of the Logistic model with second order autoregressive errors to describe the pear fruit length increase.
Limited by the length of the paper, Table 2 merely lists the quantile vector autoregressive regression model results at representative quantiles (25%, 50%, and 75%, p=3).
Two famous econometricians formulated the strategy of forecasting a times series called the Box-Jenkins method named after the statisticians George Box and Gwilym Jenkins, [11] this method applies autoregressive moving average (ARMA) or autoregressive integrated moving average (ARIMA) models to find the best fit of a time-series model to past values of a time series.
Third, assuming that output gap is autoregressive process, one period ahead forecast may be upward biased in recession and downward biased in boom.
Brandt and Jones (2002) stated that a range-based exponential generalized autoregressive conditional heteroskedastic (EGARCH) model provides better results for out-of-sample volatility forecasting than a return-based model.

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