- In the case of the Chow test, the null hypothesis being tested is:
- There is a single structural break at a known point in the sample.
- The complete coefficient vector in the regression model is the same in each regime.
- All of the slope coefficients in the regression model are the same in each regime.
- The variance of the error term is the same before and after a possible break-point.

- If we are applying the Chow test in the context of a regression model that has a lagged value of the dependent variable as a regressor, then:
- The test statistic is F-distributed if the null hypothesis is true.
- The test statistic will be asymptotically F-distributed if the null hypothesis is true.
- The test statistic will be asymptotically Chi-Square distributed with (n-k) degrees of freedom if the null hypothesis is true.
- k times the test statistic will be asymptotically Chi-Square distributed with k degrees of freedom if the null hypothesis is true.

- The "forecast period" version of the Chow test is used when:
- The location of the potential break-point is such that there are insufficient degrees of freedom to allow the model to be estimated separately over either of the sub-samples.
- The location of the potential break-point is such that there are insufficient degrees of freedom to allow the model to be estimated separately over one of the sub-samples.
- The location of the potential break-point is unknown.
- The location of the potential break-point is close to one end of the sample.

- The correct application of the Chow test requires the following, with respect to the variance of the error term in the regression model:
- The value of this variance must be known.
- This variance is the same for both of the regimes.
- This variance must be estimated consistently.
- None of the above.

- Check the EViews regression output located here. The following is true:
- The null hypothesis for the Chow test is that the β vector is the same over the periods 1960 to 1967, and 1968 to 1983, and we would reject this hypothesis at the 5% significance level.
- The null hypothesis for the Chow test is that the β vector is the same over the periods 1960 to 1968, and 1969 to 1983, and we would not reject this hypothesis at the 5% significance level.
- The null hypothesis for the Chow test is that the β vector is the same over the periods 1960 to 1968, and 1969 to 1983, and we would treat (3*1.596454) as being asymptoticaly Chi-Square with 3 degrees of freedom in order to apply the test.
- The null hypothesis for the Chow test is that the β vector is the same over the periods 1960 to 1967, and 1968 to 1983, and we would not reject this hypothesis at the 5% significance level.

- When we wrongly include an extra regressor in a regression model that is otherwise properly specified:
- The OLS estimator of β is unbiased and more efficient than if we had not included this extra variable.
- The OLS estimator of β is biased and inefficient.
- The OLS estimator of β is unbiased but inefficient.
- The OLS estimator of β is biased but has lower mean squared error than if we had not included this extra variable.

- Suppose that we have a standard regression model that satisfies all of the usual assumptions. However, we wrongly include an extra regressor, and at the same time we wrongly omit a relevant regressor. In this case
- The OLS estimator of β will be both biased and inefficient.
- The OLS estimator of β will be both inefficient and inconsistent.
- The OLS estimator of β will be both biased and inconsistent.
- The OLS estimator of β will be biased but consistent.

- Suppose that we have a standard regression model that satisfies all of the usual assumptions, but has a lagged value of the dependent variable as one of the regressors. If we wrongly omit a relevant regressor, then:
- The OLS estimator for β will be more biased than if we had included this omitted variable.
- The OLS estimator for β will be more biased than if we had included this omitted variable, and not included the lagged dependent variable in this model.
- The OLS estimator for β will be less biased than if we had included this omitted variable.
- The OLS estimator for β will be biased, but will still be consistent as long as the errors are serially independent.

- If we wrongly omit a regressor from an otherwise well-specified regression model then:
- The (usually unbiased) estimator for the variance of the error term will be biased, unless the omitted regressor is uncorrelated with the included regressors.
- The (usually unbiased) estimator for the variance of the error term will be negatively biased, unless the omitted regressor is uncorrelated with the included regressors.
- The (usually unbiased) estimator for the variance of the error term will be positively biased, unless the omitted regressor is uncorrelated with the included regressors.
- The (usually unbiased) estimator for the variance of the error term will be positively biased.

- Check the EViews regression output located here. The following is true:
- The two models are "nested" and we would prefer Equation 2 over Equation 1 on the basis of the Akaike and Schwarz criteria.
- The two models are "non-nested" and we would prefer Equation 2 over Equation 1 on the basis of the Akaike and Schwarz criteria.
- The two models are "non-nested" and we would prefer Equation 1 over Equation 2 on the basis of the Akaike and Schwarz criteria.
- The two models are "non-nested", but we can't choose between them on the basis of the Akaike and Schwarz criteria because Equation 2 uses a different sample from Equation 1.

- If we wrongly omit one or more regressors from our regression model then:
- The usual unbiased estimator of the variance of the error term will be inconsistent.
- The usual unbiased estimator of the variance of the error term will be biased, but consistent.
- The usual t-statistics will no longer be t-distributed under the null.
- Both A and C are correct.

- If the correct regression model is one that is a non-linear function of the parameters, but instead we fit a model that is linear in the parameters, then:
- The OLS estimator for β will be biased and inconsistent.
- The OLS estimator for β will be biased but consistent.
- The OLS estimator for β will be unbiased but inconsistent.
- None of the above.