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Author Question: What is an autoregressive forecasting model? What will be an ideal ... (Read 126 times)

mia

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on: Jun 24, 2018
What is an autoregressive forecasting model?
 
  What will be an ideal response?

Question 2

Which of the following explains the capacity constraint for the Kansas plant?
 
  A) X41 + X42 + X43 + X44  3,000Y1
  B) X41 + X42 + X43 + X44  3,000Y2
  C) X41 + X42 + X43 + X44  3,600
  D) X41 + X42 + X43 + X44  1,400
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Anna

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Reply #1 on: Jun 24, 2018
Answer to Question 1

An autoregressive forecasting model incorporates correlations between consecutive values in a time series by using explanatory variables that are lagged values of the dependent variable. A first-order autocorrelation refers to the correlation among data values one period apart, a second-order autocorrelation refers to the correlation among data values two periods apart, and so on. Autoregressive models improve forecasting when autocorrelation is present in data. A first-order autoregressive model is:
Yi = a0 + a1Yi -1 + i
where Yi is the value of the time series in period i and i is a nonautocorrelated random error term having 0 mean and constant variance. A second-order autoregressive model is:
Yi = a0 + a1Yi -1 + a2Yi -2 + i
Additional terms may be added for higher-order models.

Answer to Question 2

B


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