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Questions and Answers
What are the consequences of positively autocorrelated error terms in a regression model?
What are the consequences of positively autocorrelated error terms in a regression model?
- s(bk) calculated according to ordinary least squares procedures may overestimate the true standard deviation of the estimated regression coefficient.
- Estimated regression coefficients become biased and inefficient.
- MSE may seriously overestimate the variance of the error terms.
- Estimated regression coefficients are still unbiased, but may be quite inefficient. (correct)
What is a major cause of positively autocorrelated error terms in business and economic regression applications involving time series data?
What is a major cause of positively autocorrelated error terms in business and economic regression applications involving time series data?
- Omission of one or several key variables from the model. (correct)
- Inclusion of too many key variables in the model.
- Using a different regression method.
- Assuming independence of error terms.
What is the term used to describe error terms that are correlated over time in time series data?
What is the term used to describe error terms that are correlated over time in time series data?
- Autocorrelated (correct)
- Independent
- Homoscedastic
- Heteroscedastic
What assumption about error terms is often not appropriate for business and economic regression applications involving time series data?
What assumption about error terms is often not appropriate for business and economic regression applications involving time series data?
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What test is commonly used to detect the presence of autocorrelation in the error terms of a regression model?
What test is commonly used to detect the presence of autocorrelation in the error terms of a regression model?
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