54 CFA Level 2 Practice Questions on Quantitative Methods

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Describe the change in Y for a one unit change in X= covariance xy / variance x

• Slope coefficient (b hat)
• partial slope coefficient
• Multicollinearity
• log-linear trend models

none of the coefficients is significantly different than zero while F-test is statistically significant and the R square is high

• Ways to correct for multicollinearity
• ways to detect multicollinearity
• ways to detect serial correlation
• Multicollinearity

1. Parameter Instability (linear relationships can change over time, data from yesterday may not be useful for forecast)2. Even if it does - its usefulness is limited because other market participants will know as well3. If the assumption do not hold - the interpretation and test of hypotheses may not hold

• Limitations of Regression
• Limitation of Correlation Analysis
• Assumption of Linear Regression
• mean reversion

whether at least one independent variable in a set of independent variables explains a significant portion of the variation of the dependent variable = MSR / MSE

• ANOVA
• first differencing
• Multicollinearity
• F-statistic in ANOVA

model based on logistic distribution to describe qualitative DV

• logit model
• ANOVA
• unit root
• random walk

situation in which terms are correlated with one anotherpositive autocorrelation: positive regression error in one time period increases the probability of observing a positive regression error negative autocorrelation: positive error in one period increases the probability of observing a negative error

• Correlation Coefficient (r)
• standard error of the estimated autocorrelation
• Serial correlation OR autocorrelation
• Ways to correct serial correlation

used to compare the accuracy of autoregressive models in forecasting out-of-sample values smaller result suggest better accuracy

• mean reverting level
• root mean squared error (RMSE)
• Sum of the squared error (SSE)
• Sum of Squared Errors (SSE)

= 1 / sq root T where T is the number of observations

• ways to detect serial correlation
• T-Test for estimated regression
• standard error of the estimated autocorrelation
• Ways to correct serial correlation

1. using robust standard error (White-corrected st. errors to recalculate t-statistics)2. using generalized least squares (modifying the equation)

• unconditional heteroskedasticity
• Ways to correct serial correlation
• conditional heteroskedasticity
• Ways to correct heteroskedasticity