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I am practicing random forest regressions with monthly US inflation rates as sample data. I am predicting the inflation rate simply using its past month value. My last data point is for May 22 when monthly inflation was 0.97%, the mean for the whole sample is 0.29%. The predicted inflation rate for June is monotonically decreasing in the maximum tree depth. For example when I set it to 5, the prediction is 0.72% but it is merely 0.33% when I set the max depth to 20, which is only a bit above the unconditional mean. The linear OLS prediction would be 0.68%. Could someone explain the intuition behind this decline in the forecast value towards the mean?

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