The Validity of Consumer Sentiment in Small-Area Economic Forecasting: A NaÏve Bayes Analysis

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Obtaining an accurate picture of the current state and direction of the regional economy is particularly important to local decision-makers, including shopkeepers, academic institutions, and state and local government agencies. Traditional, survey-based sentiment indices have long-existed and are used for this purpose. But current abilities to source online data to map consumer sentiment has kindled interest in their usefulness in regional economic forecasting. The appeal of tailored sentiment indices and other similar online-sourced measures are their seeming immediacy and their ability to capture information in more relevant geographic and product domains - which is believed to enhance their capability of improving predictive metrics. If decision-makers are to profitably rely with reasonable confidence from the increased availability of localized sentiment indiices and traditional data. Perhaps more importantly, users will have to be assured of sentiment index validity in enhancing regional economic forecasts. We test sentiment index relevance in this paper reproducing results of a popular local forecast. Specifically, we appraise whether there are measurable improvements from the presence of a publicly available sentiment index to the New Haven Register's Economic Scorecard, a popular regional forecast model. The model is a binary directional prediction model. Succinctly, we find measurable improvements in the model's predictive accuracy of the Economic Scorecard. We speculate as to the generalizability of our results, especially regarding the use of other online-sourced now casting metrics.


Carolyne Cebrian is a student in the Doctor of Business Administration in Finance Program in the Jack Welch College of Business at Sacred Heart University.