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Unformatted text preview: errors and thus a tendency to
overaccept the null of no signiﬁcance 2 / 62 Introduction Time Series and OLS Two Dynamic Models Autocorrelation Time Series and OLS What are the likely problems when estimating the following with
OLS?
Yt = β1 + β2 Xt + β3 Xt −1 + ut
Assume that all the assumptions for model C hold.
X could be highly autocorrelated
⇒ problems with multicollinearity
i.e. high standard errors and thus a tendency to
overaccept the null of no signiﬁcance 3 / 62 Introduction Time Series and OLS Two Dynamic Models Autocorrelation Time Series and OLS What are the likely problems when estimating the following with
OLS?
Yt = β1 + β2 Xt + β3 Xt −1 + ut
Assume that all the assumptions for model C hold.
X could be highly autocorrelated
⇒ problems with multicollinearity
i.e. high standard errors and thus a tendency to
overaccept the null of no signiﬁcance 4 / 62 Introduction Time Series and OLS Two Dynamic Models Autocorrelation Time Series and OLS What are the likely problems when estimating the following with
OLS?
Yt = β1 + β2 Xt + β3 Xt −1 + ut
Assume that all the assumptions for model C hold.
X could be highly autocorrelated
⇒ problems with multicollinearity
i.e. high standard errors and thus a tendency to
overaccept the null of no signiﬁcance 5 / 62 Introduction Time Series and OLS Two Dynamic Models Autocorrelation Dynamic Models Some variables of interest are subject to substantial inertia
That is, instead of explaining the dependent variable with
the current explanatory variables, we include the lagged
explanatory variables.
The sizes of the coefﬁcients of the current and lagged
values of the explanatory variables are important. Lag
structure.
However, too many lagged explanatory variables incur
multicollinearity.
Parsimonious lag structure would be ideal. 6 / 62 Introduction Time Series and OLS Two Dynamic Models Autocorrelation Dynamic Models Some variables of interest are subject to substantial inertia
That is, instead of explaining the dependent variable with
the current explanatory variables, we include the lagged
explanatory variables.
The sizes of the coefﬁcients of the current and lagged
values of the explanatory variables are important. Lag
structure.
However, too many lagged explanatory variables incur
multicollinearity.
Parsimonious lag structure would be ideal. 7 / 62 Introduction Time Series and OLS Two Dynamic Models Autocorrelation Dynamic Models Some variables of interest are subject to substantial inertia
That is, instead of explaining the dependent variable with
the current explanatory variables, we include the lagged
explanatory variables.
The sizes of the coefﬁcients of the current and lagged
values of the explanatory variables are important. Lag
structure.
However, too many lagged explanatory variables incur
multicollinearity.
Parsimonious lag structure would be ideal. 8 / 62 Introduction Time Series and OLS Two Dynamic Models Autocorrelation Dynamic Models Some variables of interest are subject to substantial inertia
That is, instead of explaining the dependent variable with
the current explanatory variables, we include the lagged
explanatory variables.
The...
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 Spring '13
 ChristopherDougherty
 Econometrics

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