#1
What is the main goal of econometric analysis?
To explain and predict economic phenomena using data
ExplanationUnderstanding and forecasting economic behavior through statistical analysis.
#2
Which of the following is a basic assumption of the classical linear regression model?
Homoscedasticity
ExplanationAssumption of constant variance of errors across observations.
#3
What does the term 'multicollinearity' refer to in econometrics?
The correlation among independent variables
ExplanationHigh correlation between independent variables in a regression model.
#4
In econometrics, what does the term 'endogeneity' refer to?
The correlation between the error term and one or more independent variables
ExplanationRefers to correlation between independent variables and error term.
#5
Which of the following is a key assumption of the Ordinary Least Squares (OLS) method?
Homoscedasticity
ExplanationBasic assumption of OLS: constant variance of errors.
#6
In econometrics, what does the P-value represent in hypothesis testing?
The probability of committing a Type I error
ExplanationProbability of rejecting a true null hypothesis.
#7
Which of the following is NOT a method to deal with heteroscedasticity in regression analysis?
Adding more independent variables
ExplanationIncorrect; adding variables doesn't address heteroscedasticity.
#8
What is the purpose of the Durbin-Watson statistic in econometrics?
To test for autocorrelation
ExplanationAssessing the presence of serial correlation in regression residuals.
#9
What is the primary purpose of instrumental variables in econometrics?
To replace endogenous variables with exogenous ones
ExplanationUtilized to address endogeneity issues by introducing independent instruments.
#10
What is the purpose of the Ramsey RESET test in econometrics?
To test for specification errors
ExplanationExamining whether the model's functional form is correct.
#11
What is the purpose of the Akaike Information Criterion (AIC) in model selection?
To penalize models with more parameters
ExplanationBalancing model fit with complexity to prevent overfitting.