DA0-002 Data Analysis Practice Question
A financial analyst is building a model to predict stock price movements. The data is time series with daily prices. The analyst wants to use a regression model but notices that the residuals are autocorrelated. What adjustment should be made?
⚠ Common exam trap
Many candidates confuse data transformation (like differencing) with model selection, thinking that simply removing autocorrelation from the data is sufficient, when in fact the model itself must be changed to a time series framework like ARIMA to properly account for the temporal structure.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use a time series model like ARIMA instead
When residuals from a regression model on time series data exhibit autocorrelation, the standard ordinary least squares (OLS) assumptions are violated, leading to biased standard errors and unreliable inference. An ARIMA model is specifically designed to handle autocorrelated time series by explicitly modeling the autoregressive (AR) and moving average (MA) components, making it the correct adjustment to capture the temporal dependencies in stock price movements.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use a time series model like ARIMA instead
Why this is correct
ARIMA models capture autocorrelation through autoregressive and moving average components.
- ✗
Use cross-validation to validate the model
Why it's wrong here
Cross-validation does not fix autocorrelation; the model structure is still invalid.
- ✗
Add more predictors to the regression model
Why it's wrong here
Adding predictors may not remove autocorrelation; it may still violate assumptions.
- ✗
Transform the data to remove autocorrelation (e.g., differencing)
Why it's wrong here
Transforming the original data, such as differencing, is typically employed to achieve stationarity in the time series itself, removing trends and autocorrelation before model building. However, the problem specifies autocorrelated *residuals* from an already built regression model. Differencing the original data would fundamentally change the dependent variable, not directly address the issue of correlated errors within the current model structure. This approach would be appropriate if the goal was to model a stationary series directly or if the original data exhibited non-stationarity requiring transformation.
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