Consider the following multiple linear regression (MLR) model, y = β0 + β1x1 + …+ βkxk + u. Which of the following statements is correct?

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Let’s examine each statement about the multiple linear regression model y = β0 + β1x1 + … + βk xk + u in turn, evaluating whether it can be correct under standard assumptions and common violations.
Option A: 'MLR.4 can fail if the functional relationship between the explained and explanatory variables is misspecified.' This is true because the classical MLR assumes a linear functional form. If the true relationship is nonlinear or otherwise misspecified, the linear model will produce biased or inconsistent estimates for the parameters and poor predictive performance, illustrating a failure due to misspecification.
Option B: 'None of the above.' Given that other statements may be valid under certain conditions, this option claims that all the listed reasons cannot cause failure. Since at least some typical issues (such as misspecification, measurement error, endogeneity, and omitted variables) do indeed cause problems for MLR, this option is unlikely to be correct in light of the other statements.
Option C: 'All of the above.' This would be correct if every individual listed factor can cause MLR to fail. If A, D, and E (and the general considerations about misspecification and omitted variables) are all valid sources of failure, then 'All of the above' becomes a comprehensive acknowledgment of multiple failure modes.
Option D: 'MLR.4 can fail due to measurement error in an explanatory variable or when one or more explanatory variables is determined jointly with y.' Measurement error in an explanatory variable generally attenuates estimated coefficients, leading to biased estimates and reduced precision, which is a known source of failure for the standard OLS/MLR framework. Endogeneity or simultaneous determination (where an explanatory variable is correlated with the error term or with y through a joint determination mechanism) also causes biased, inconsistent estimates, representing another failure mode.
Option E: 'MLR.4 can fail if an important explanatory factor, that is correlated with x1, x2, … ,xk, is omitted.' Omitted variable bias arises when a relevant variable is left out and is correlated with included regressors, causing biased estimates of the coefficients and potentially misinforming inferences, a classic failure mode in MLR.
In summary, each of A, D, and E describes a plausible way the MLR framework can fail in practice due to misspecification, measurement error or endogeneity, and omitted variable bias. B is unsupported because there are well-known scenarios that cause problems, and C follows if all the listed issues are indeed valid sources of failure. The overall assessment is that multiple distinct failure mechanisms can affect MLR, making All of the above a plausible categorization in this context.

Consider the following multiple linear regression (MLR) model, y = β0 + β1x1 + …+ βkxk + u. Which of the following statements is correct?

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