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What (who) is autoregressive parameter - definition

STATISTICAL MODEL USED IN TIME SERIES ANALYSIS

Autoregressive moving average; ARMAX; Autoregressive moving average model; ARMA model; Autoregressive moving-average model; Autoregressive-moving-average model

Parameter (computer programming)

IN COMPUTER PROGRAMMING, SPECIAL KIND OF VARIABLE THAT HOLDS DATA THAT WAS PASSED AS AN ARGUMENT TO A SUBROUTINE

Argument (computer science); Argument (programming); Parameter (programming); Formal parameter; Actual parameter; Parameters (computer science); Formal parameters; Function parameter; Argument (computing); Parameter (computer science); Parameter (computing); Output parameter; Out parameter; Return parameter; Argument (computer programming); Input parameter; Input value; Output value; Actual parameters

In computer programming, a parameter or a formal argument is a special kind of variable used in a subroutine to refer to one of the pieces of data provided as input to the subroutine. These pieces of data are the values of the arguments (often called actual arguments or actual parameters) with which the subroutine is going to be called/invoked.

https://en.wikipedia.org/wiki/Parameter_(computer_programming)

Statistical parameter

QUANTITY THAT INDEXES A PARAMETRIZED FAMILY OF PROBABILITY DISTRIBUTIONS

Numerical parameter; Population parameter; Statistical measure; Numeric parameter; Statistical parameters; True value

In statistics, as opposed to its general use in mathematics, a parameter is any measured quantity of a statistical population that summarises or describes an aspect of the population, such as a mean or a standard deviation. If a population exactly follows a known and defined distribution, for example the normal distribution, then a small set of parameters can be measured which completely describes the population, and can be considered to define a probability distribution for the purposes of extracting samples from this population.

https://en.wikipedia.org/wiki/Statistical_parameter

Autoregressive–moving-average model

In the statistical analysis of time series, autoregressive–moving-average (ARMA) models provide a parsimonious description of a (weakly) stationary stochastic process in terms of two polynomials, one for the autoregression (AR) and the second for the moving average (MA). The general ARMA model was described in the 1951 thesis of Peter Whittle, Hypothesis testing in time series analysis, and it was popularized in the 1970 book by George E.

https://en.wikipedia.org/wiki/Autoregressive–moving-average_model

Wikipedia

Autoregressive–moving-average model

In the statistical analysis of time series, autoregressive–moving-average (ARMA) models provide a parsimonious description of a (weakly) stationary stochastic process in terms of two polynomials, one for the autoregression (AR) and the second for the moving average (MA). The general ARMA model was described in the 1951 thesis of Peter Whittle, Hypothesis testing in time series analysis, and it was popularized in the 1970 book by George E. P. Box and Gwilym Jenkins.

Given a time series of data $X_{t}$ , the ARMA model is a tool for understanding and, perhaps, predicting future values in this series. The AR part involves regressing the variable on its own lagged (i.e., past) values. The MA part involves modeling the error term as a linear combination of error terms occurring contemporaneously and at various times in the past. The model is usually referred to as the ARMA(p,q) model where p is the order of the AR part and q is the order of the MA part (as defined below).

ARMA models can be estimated by using the Box–Jenkins method.

https://en.wikipedia.org/wiki/Autoregressive–moving-average model