A model where the current value of a variable depends upon only the values that the variable took in previous periods plus an error term is called:Group of answer choicesA periodic lag modelAn autoregressive modelAn autoregressive integrated moving average modelAn autoregressive moving average model

The correct answer is: An autoregressive model.

This model is a type of random process; as such, it is often used to model and forecast time series data. Autoregressive models have the characteristic that they "regress" on their own previous values. That is, the value of the variable at a certain time depends on its values at previous times. The term "autoregressive" describes this property, which can be associated with the kind of "memory" that these models possess.

The correct answer is: An autoregressive model.

This model is a type of random process; as such, it is often used to model and forecast time series data. Autoregressive models have the characteristic that they use a combination of past values of the variable to predict its future values. The term 'autoregressive' indicates that it is a regression of the variable against itself. Thus, an autoregressive model of order p can be written as:

Yt = c + φ1Yt-1 + φ2Yt-2 + ... + φpYt-p + Et

where:

• Yt is the value of the variable at time t
• c is a constant
• φ1, φ2, ..., φp are the parameters of the model
• Et is white noise.