Quantitative Analysis and Econometrics Flashcards
7 cards from real CEA practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 7 Quantitative Analysis and Econometrics flashcards as text
The Gauss-Markov theorem guarantees that OLS estimators are BLUE. What does BLUE stand for?
Answer: Best (minimum variance) Linear Unbiased Estimators
Under the Gauss-Markov assumptions, OLS produces the Best (minimum variance) Linear Unbiased Estimator among all linear unbiased estimators.
When the White test for heteroscedasticity is significant, the recommended correction is typically:
Answer: Use heteroscedasticity-robust (White) standard errors
White's heteroscedasticity-consistent standard errors correct inference without requiring knowledge of the specific form of heteroscedasticity.
In a difference-in-differences (DiD) design, the key identifying assumption is:
Answer: Treatment and control groups would have followed parallel trends absent treatment
The parallel trends assumption states that without the intervention, the treated and control groups would have changed by the same amount over time.
A regression F-statistic for joint significance has a p-value of 0.32. The correct interpretation is:
Answer: The predictors jointly fail to explain significant variation in the outcome
A p-value of 0.32 exceeds conventional significance levels, so we fail to reject the null hypothesis that all slope coefficients are simultaneously zero.
Which forecasting error measure is most appropriate for comparing models applied to variables with different scales or units?
Answer: Mean Absolute Percentage Error (MAPE)
MAPE expresses errors as a percentage of actual values, making it scale-independent and suitable for comparing forecast accuracy across differently-scaled variables.
In principal component analysis (PCA) applied to economic data, the first principal component is defined as:
Answer: The linear combination of original variables that maximizes explained variance
The first principal component is the linear combination of original variables with weights chosen to maximize the variance of the resulting scores.
The Akaike Information Criterion (AIC) penalizes model complexity primarily to:
Answer: Prevent overfitting by trading off goodness-of-fit against number of parameters
AIC = 2k - 2ln(L), where k is the number of parameters; the penalty term 2k discourages adding parameters that only marginally improve fit.