# Yt 1 2 xt 3 xt 1 ut assume that all the assumptions

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Unformatted text preview: errors and thus a tendency to over-accept 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 over-accept 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 over-accept 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 over-accept 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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