Classical linear regression model assumptions and diagnostics

Below are the consequences of violating the CLRM assumptions, except:

• the coefficient can not be estimated

• the coefficient estimates are wrong

• the associated standard errors are wrong

• the distribution that we assumed for the test statistics will be inappropriate

Which of the following would NOT be a potential remedy for the problem of multicollinearity between regressors?

• Removing one of the independent variables

• Transforming the data into natural logarithms

• Transforming two of the independent variables into ratios

• Collecting higher frequency data on all of the variables

Which of the following is NOT a good reason for including lagged variables in a regression?

• Slow response of the dependent variable to changes in the independent variables

• Over-reactions of the dependent variables

• The dependent variable is a centred moving average of the past 4 values of the series

• The residuals of the model appear to be non-normal.

A normal distribution has coefficients of skewness and excess kurtosis which are, respectively

• 0 and 0

• 0 and 3

• 3 and 0

• Will vary from one normal distribution to another.

What would be the consequences for the OLS estimator if autocorrelation is present in a regression model but ignored?

• It will be biased

• it will be inconsistent

• it will be inefficient

• all of them are correct

If OLS is used in the presence of heteroscedasticity, which of the following will be likely consequences?

• Coefficient estimates may be misleading

• Hypothesis tests could reach the wrong conclusions

• Forecasts made from the model could be biased

• coefficient may inappropriate

If a residual series is negatively autocorrelated, which one of the following is the most likely value of the Durbin–Watson statistic?

• close to zero

• close to two

• close to four

• close to one

The graphs above are time series plots of residuals from two separate regressions. Which of these combinations is true?

• A shows negative autocorrelation and B shows positive autocorrelation

• A shows positive autocorrelation and B shows negative autocorrelation

• A shows heteroscedasticity and B shows homoscedasticity

• A shows homoscedasticity and B shows heteroscedasticity

what method can be used as remedy for heteroscedasticity and autocorrelation problem?

• Generalized least square

• Multiple regression

• Bivariate regression

• adding the lag of dependent and independent variables