Basic Regression Analysis with Time Series Data - Finite Sample Properties of OLS under Classical Assumptions
3 important questions on Basic Regression Analysis with Time Series Data - Finite Sample Properties of OLS under Classical Assumptions
What are the classical linear model assumptions in time series regression?
TS1 (Linear in parameters)
TS2 (No perfect collinearity)
TS3 (Zero conditional mean) E[u|X]=0
TS4 (Homoskedasticity) var(u|X)=o2
TS5 (No serial correlation) correlation(u(t),u(s)|X)
TS6 (Normality) N(0,o2)
TS2 (No perfect collinearity)
TS3 (Zero conditional mean) E[u|X]=0
TS4 (Homoskedasticity) var(u|X)=o2
TS5 (No serial correlation) correlation(u(t),u(s)|X)
TS6 (Normality) N(0,o2)
Which TS assumptions have to hold to get unbiasedness OLS?
TS1-TS3 gives E[^Bj|X]=Bj
Which variables hold with TS1-TS5 Gauss-Markov assumptions?
OLS sampling variances and unbiased estimation of o2.
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