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Basic Question 10 of 11
Out-of-sample testing is essential for assessing a model's ability to:
A. Fit historical data.
B. Achieve high training accuracy.
C. Generalize to new, unseen data.
D. Minimize training time.
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Learning Outcome Statements
describe the structure of an autoregressive (AR) model of order p and calculate one- and two-period-ahead forecasts given the estimated coefficients;
explain how autocorrelations of the residuals can be used to test whether the autoregressive model fits the time series;
explain mean reversion and calculate a mean-reverting level;
contrast in-sample and out-of-sample forecasts and compare the forecasting accuracy of different time-series models based on the root mean squared error criterion;
CFA® 2025 Level II Curriculum, Volume 1, Module 5.