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10. Basic Regressions with Times Series Data

10. Basic Regressions with Times Series Data. 10.1 The Nature of Time Series Data 10.2 Examples of Time Series Regression Models 10.3 Finite Sample Properties of OLS Under Classical Assumptions 10.4 Functional Form, Dummy Variables, and Index Numbers 10.5 Trends and Seasonality.

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Assumption MLR.3 Notes (No Perfect Collinearity)

Assumption MLR.3 Notes (No Perfect Collinearity). Perfect Collinearity can exist if: One variable is a constant multiple of another Logs are used inappropriately One variable is a linear function of two or more other variables

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10. Basic Regressions with Times Series Data

Assumption MLR.3 Notes (No Perfect Collinearity)