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
A regression discontinuity design (RDD) estimates causal effects by exploiting:
Answer: A discontinuous jump in treatment probability at a known cutoff value
RDD compares outcomes just above and just below a cutoff where treatment assignment changes sharply, using the discontinuity as a source of quasi-random variation.
In a GARCH(1,1) model used for financial econometrics, the conditional variance depends on:
Answer: The previous squared error term and the previous period's conditional variance
GARCH(1,1) specifies conditional variance as a function of both the lagged squared shock (ARCH term) and the lagged conditional variance (GARCH term).
The concept of identification in econometrics refers to:
Answer: The ability to consistently estimate model parameters from the available data
Identification means the model parameters can be uniquely determined from the data's population moments; without it, multiple parameter values fit equally well.
When applying the Box-Jenkins methodology to build an ARIMA model, the correct sequence of steps is:
Answer: Identification → Estimation → Diagnostic checking → Forecasting
Box-Jenkins follows: identify appropriate ARIMA orders using ACF/PACF, estimate parameters, check diagnostics with residual tests, then use the validated model to forecast.
An economist uses two-stage least squares (2SLS) because OLS yields biased estimates. The bias in OLS arises from:
Answer: Correlation between an explanatory variable and the error term (endogeneity)
Endogeneity — when a regressor is correlated with the error term — causes OLS to be inconsistent; 2SLS uses instruments to purge the endogenous variation.
In quantile regression, the median regression (tau = 0.5) differs from OLS in that it minimizes:
Answer: The sum of absolute deviations
Quantile regression minimizes a weighted sum of absolute deviations; at the median, this reduces to the unweighted sum of absolute residuals (LAD regression).
The Granger causality test is best described as determining whether:
Answer: Past values of X improve the forecast of Y beyond Y's own past values
Granger causality tests whether including lagged X significantly reduces forecast errors for Y compared to using only Y's own lags — it is a predictive, not structural, concept.