autoregressive model

The autoregressive model is an alternative that may have only a few coefficients if the corresponding moving average has many.

These time series are generated by autoregressive models.

The first order autoregressive model, , has a unit root when .

Next, consider the autoregressive model proposed by Walker.

For example, a common parametric technique involves fitting the observations to an autoregressive model.

In statistics, a unit root test tests whether a time series variable is non-stationary using an autoregressive model.

MESA operates by first fitting an autoregressive model to the data.

The autoregressive model specifies that the output variable depends linearly on its own previous values.

An autoregressive model is essentially an all-pole infinite impulse response filter with some additional interpretation placed on it.

Several classes of nonlinear autoregressive models formulated for time series applications have been threshold models.

The predictive performance of the autoregressive model can be assessed as soon as estimation has been done if cross-validation is used.

An autoregressive model can thus be viewed as the output of an all-pole infinite impulse response filter whose input is white noise.

Autoregressive model of order p, denoted as AR(p), has the following form:

The data was filtered to eliminate long trends; autocorrelation was determined using an ordinary least-squares fit to a first order autoregressive model.

Autoregressive Models:

Estimate the best fitting autoregressive model AR(q) .

Often, ordinary least squares (OLS) is used to estimate the slope coefficients of the autoregressive model.

Constants of autoregressive models that are based on the fast Fourier transform algorithm(FFT).

A Bayesian extension of the minimum AIC procedure of autoregressive model fitting.

Partial autocorrelation plots (Box and Jenkins, Chapter 3.2, 2008) are a commonly used tool for identifying the order of an autoregressive model.

Two commonly used forms of these models are autoregressive models (AR) and moving average (MA) models.

Autoregressive model / (FS:C)

Fitting the MA estimates is more complicated than with autoregressive models (AR models) because the lagged error terms are not observable.

Between the World Wars, Herman Wold developed a representation of stationary stochastic processes in terms of autoregressive models and a determinist trend.

The optimal finite sample tests for a unit root in autoregressive models were developed by John Denis Sargan and Alok Bhargava.