The period saw a shift toward handling large predictor sets by summarizing information through factor-analytic and dimension-reduction approaches, notably using components to stabilize forecasts in high-dimensional settings. Emphasis on model uncertainty and data mining grew, with robust inference and model-averaging frameworks addressing ambiguous model structure. Ensemble methods began delivering probabilistic forecasts, while regression modeling strategies evolved into practical, widely applicable tools for building and validating predictive models. Historical Significance: These developments forged a durable paradigm that treats dimensionality, uncertainty, and ensemble reasoning as central to predictive modeling, influencing both econometrics and statistics and foreshadowing later advances in machine learning. The period's innovations provided standard techniques for evaluating forecast performance, integrating diverse information sources, and producing probabilistic predictions from ensembles, shaping subsequent research across domains.