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Free CFA Quantitative Methods Practice Questions & Answers

1,200 exam-style Quantitative Methods questions. Pick your answer, hit Check answer, and see the worked solution — free to start, no signup.

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Question 841

A stationary series has autocovariances that depend only on:

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Question 842

Using a non‑stationary series directly in regression often leads to:

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Question 843

A random walk with drift differs from a pure random walk because it:

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Question 844

A series that fluctuates around a constant mean with constant variance and no trend is:

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Question 845

If first‑differencing a series makes it stationary, the original series is:

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Question 846

A non‑stationary series with a unit root is often modeled by:

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Question 847

A stationary series is required for:

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Question 848

A linear trend model assumes the variable:

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Question 849

If a variable grows at a constant percentage rate, the natural log of the variable over time will appear:

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Question 850

A high R² in a trend regression mainly indicates:

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Question 851

A log‑linear trend model predicts future values by:

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Question 852

A linear trend model is likely inappropriate if the data:

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Question 853

A trend model with strongly serially correlated residuals suggests:

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Question 854

A log‑linear model is preferred over a linear model when:

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Question 855

A trend model with a Durbin–Watson statistic near zero likely has:

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Question 856

A limitation of simple trend models is that they often:

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Question 857

A Durbin–Watson statistic close to 2 suggests:

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Question 858

A Durbin–Watson statistic far below 2 suggests:

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Question 859

Durbin–Watson is not valid when the regression includes:

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Question 860

Serial correlation in regression errors mainly affects:

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Question 861

A model with serially correlated errors violates the assumption of:

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Question 862

Residual autocorrelation can be tested by:

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Question 863

A regression model shows a Durbin–Watson statistic of 0.25. This most likely indicates:

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Question 864

Residual autocorrelation is considered significant when:

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Question 865

If several residual autocorrelations are statistically significant, the model is likely:

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Question 866

An AR(1) model predicts the next value using:

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Question 867

An AR(2) model includes:

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Question 868

For an AR(1) model to be stationary, the lag coefficient must be:

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Question 869

A lag coefficient of 0.95 in an AR(1) model indicates:

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Question 870

A lag coefficient greater than 1 in absolute value implies the series is:

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Free CFA Quantitative Methods Practice Questions & Answers

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