True or false?

More questions will follow.

Chapter 2: An introduction to linear regression

2.1. Under the Gauss-Markov conditions, OLS can be shown to be BLUE. The phrase "linear" in this acronym refers to the fact that we are estimating a linear model. true or false?

2.2. In order to apply a t-test, the Gauss-Markov conditions are strictly required. true or false?

2.3. A regression of the OLS residual upon the regressors included in the model by definition yields an R-squared of zero. true or false?

2.4. The hypothesis that the OLS estimator is equal to zero can be tested by means of a t-test. true or false?

2.5. From asymptotic theory we learn that - under appropriate conditions - the error terms in a regression model will be approximately normally distributed if the sample size is sufficiently large. true or false?

2.6. If the absolute t-value of a coefficient is smaller than 1.96, we can accept the null hypothesis that the coefficient is zero, with 95% confidence. true or false?

2.7. Because OLS provides the BEST linear approximation of a variable y from a set of regressors, OLS also gives BEST linear unbiased estimators for the coefficients of these regressors. true or false?

2.8. If a variable in a model is significant at the 10% level, it is also significant at the 5% level. true or false?

Chapter 3: Interpreting and comparing regression models

3.1. Consider two alternative models that are nested. If we compare the models on the basis of the adjusted R squared and an F-test, the F-test will prefer the extended model more often than the adjusted R-squared. true or false?

Chapter 4: Heteroskedasticity and autocorrelation

4.1. If the error terms in a linear model exhibit heteroskedasticity but no autocorrelation, Newey-West standard errors are identical to White standard errors. true or false?

4.2. The assumption of homoskedasticity states that the variance of the OLS residuals is constant. true or false?

Chapter 5: Endogeneity, instrumental variables and GMM

5.1. Consider a regression model with two endogenous regressors x1 and x2. An instrumental variables estimator that uses z1 as instrument for x1 and z2 as instrument for x2 is identical to one that uses z2 as instrument for x1 and z1 as instrument for x2. true or false?

5.2. In general, an instrumental variables estimator is less accurate than the OLS estimator.  true or false?

Chapter 6: Maximum likelihood estimation and testing

6.1. Asymptotically, the maximum likelihood estimator for s² in a normal linear regression model is at least as efficient as the OLS estimator s² . true or false?

Chapter 7: Models with limited dependent variables

7.1. If data are generated by means of a tobit model, all model parameters can be estimated consistently but inefficiently using probit maximum likelihood, after setting all positive values of the dependent variable to 1. true or false?

Chapter 8: Univariate time series models

8.1. A time series process with a single unit root is a random walk. true or false?

8.2. An autoregressive model can be estimated unbiasedly by OLS. true or false?

8.3. An AR(1) model with q=0.5 and ARCH(1) errors is stationary. true or false?

8.4. A nonstationary time series model is characterized by the presence of at least one unit root. true or false?

Chapter 9: Multivariate time series models

Chapter 10: Models based on panel data

10.1. For fixed T and large N, the fixed effects estimator in a linear model with a lagged dependent variable is inconsistent, because of an incidental parameters problem. true or false?

10.2. In some cases, the OLS estimator in a linear panel data model may be preferred to the random effects estimator. true or false?