Autoregressive distributed lag models include: a. current and lagged values of the error term. b. current and lagged values of the residuals. c. lags of the dependent variable, and lagged values of additional predictors. d. lags and leads of the dependent variable.

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

What type of a process is ? Group of answer choicesAn autoregressive modelAn autoregressive integrated moving average modelA periodic lag modelAn autoregressive moving average model

Which of the following is true for an autoregressive model?Answer choicesSelect only one optionREVISITIn an autoregressive model, the value from a time series is regressed on future values from that same time seriesIn an autoregressive model, the value from a time series is regressed on previous values from that same time seriesIn an autoregressive model, the value from a time series is regressed on previous values from a different time seriesIn an autoregressive model, the value from a time series is regressed on future values from a different time series

The lag occurs because we need time to collect and analyze data.

Which technique is used to evaluate the residuals of a time series model?Review LaterAutocorrelation analysisDifferencingMoving averageSeasonal adjustment