Suppose you fit a time series model by differencing the outcome variable yt and the input variable xt and includes lags, such as ∇yt = β1∇yt−1 + β2∇xt + β3∇xt−1 + t. where ∇ is the first differences operator. For this model,
(a) What assumptions are you making about the dynamic relationship of yt and xt in fitting such a model?
(b) What happens to the properties of your dynamic model estimates if the errors are still serially correlated versus when they are white noise?
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Order Paper Now(c) What restrictions are you imposing on the dynamic relationships? Are these valid? How coul
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