10. What would be the consequences for the OLS estimator if heteroscedasticity is present in a regression model, but is ignored? a) It will be biased b) It will be inconsistent c) It will be efficient d) It will be unbiased and consistent 11. Which of the following will result from including an irrelevant variable in a model? a) The OLS estimates are still unbiased and consistent. b) The hypothesis testing procedures remain valid. c) The parameter estimates will be generally inefficient. d) All of the above 12. Stationarity means that the a) error terms are not correlated. b) distribution of the time series variable does not change over time. c) time series has a unit root. d) time series are a random walk