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We apply a hurdle distributed multinomial regression to PBC communication texts in real time, addressing the ultrahigh dimensionality, sparsity, and look\u2010ahead biases. In addition, we embed text\u2010based indices into mixed\u2010data sampling (MIDAS)\u2010type models and conduct forecast combinations for prediction. Our results argue that the predictive information from communication texts improves the real\u2010time out\u2010of\u2010sample prediction performance. 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