One practical interpretation of time series models is that they represent the impact of new information on the variables we are modeling. For…

Question Answered step-by-step One practical interpretation of time series models is that they representthe impact of new information on the variables we are modeling.  For example, a p-order autoregressive model contains p lags to the dependent variable that represents the impact of past information, and the model error term represents the impact of new information. In a covariance stationary model, the impact of past information diminishes over time.  In contrast, the impact of past information does not diminish over time in a random walk model, and an information shock remains in the time series forever.  Some asset price series may be characterized by a random walk model — does it make sense to you that an information shock 50 years ago is still reflected in the asset price?  Is this a realistic feature of the statistical model? Math Statistics and Probability BADM 682 Share QuestionEmailCopy link Comments (0)

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