In a predictive regression model, in-sample R-squared is computed based on

Question

BlackTom AI · Accepted Answer

In this question, we are evaluating what in-sample R-squared is computed from in a predictive regression model.
Option 1: 'all the data and the assessed predictability usually under-estimates the true predictability.' This is misleading because, in-sample R-squared is derived from how well the model fits the training data, and it often overstates rather than understates predictive ability due to overfitting, not underestimation.
Option 2: 'rolling data and it is often positive.' Rolling data refers to a moving-window approach across time, which is not the standard definition of in-sample R-squared. While a rolling approach can yield positive values, this option mischaracterizes the computation basis of in-sample R-squared.
Option 3: 'all the data and the assessed predictability is unrealistic.' Correct. In-sample R-squared uses all available data to fit and evaluate the model, which can inflate the apparent fit due to overfitting and sample-specific patterns, making the assessed predictability potentially unrealistic when generalizing to new data.
Option 4: 'rolling data and it is often negative.' As with option 2, this misstates the core concept of in-sample R-squared and introduces a less typical scenario (negative values) tied to a rolling window, which is not the standard in-sample calculation.
Overall, the key point is that in-sample R-squared is computed using all the data to fit the model, and this can give an overly optimistic or unrealistic impression of predictive performance because it capitalizes on noise and idiosyncrasies in the sample.

In a predictive regression model, in-sample R-squared is computed based on 单项选择题

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