We reveal thepsychological bias of economic agents in their judgments of future economic conditions by applying the behavioral economics and weakly supervised learning  . In the Economy Watcher Survey, which is a dataset published by the Japanese government, there are assessments of current and future economic conditions by people with various occupations  . Although this dataset gives essential insights regarding economic policy to the Japanese government and the central bank of Japan, there is no clear definition of future economic conditions. Hence, in the survey, respondents answer their assessments based on their interpretations of the future. In our research, we classify the text data using learning from positive and unlabeled data (PU learning), which is a method of weakly supervised learning. The dataset is composed of several periods, and we develop a new algorithm of PU learning for efficient training with the dataset . Through empirical analysis, we show the interpretation of the classification results from the viewpoint of behavioral economics.
We reveal the different interpretations of the future in their judgments of future economic conditions by applying weakly supervised learning and text mining . In the Economy Watcher Survey, which is a market survey published by the Japanese government, there are assessments of current and future economic conditions by people from various fields . Although this survey provides insights regarding an economic policy for policymakers in Japan, there is no clear definition of the future, in future economic conditions. Hence, in the survey, respondents make their assessments based on their interpretations of the future. In our research, we separate the assessments of future economic conditions into near and distant future economic conditions using learning from positive and unlabeled data (PU learning), which is weakly supervised learning. The dataset is composed of several periods, and we develop a PU learning algorithm for efficient training , using the dataset with the time series . Through empirical analysis, we interpret the classification results from the viewpoint of economics.
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thepsychological bias of economic agents
the different interpretations of the future
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the behavioral economics and
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and text mining
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dataset
market survey
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assessments of current and future economic conditions by people with various occupations
assessments of current and future economic conditions
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by people from various fields
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dataset gives essential insights regarding economic policy to the Japanese government and the central bank of
survey provides insights regarding an economic policy for policymakers in
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future
the future, in future
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answer
make
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classify the text data
separate the assessments of future economic conditions into near and distant future economic conditions
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a method of
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new algorithm of PU learning
PU learning algorithm
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with the dataset
, using the dataset with the time series
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show the interpretation of the
interpret the
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behavioral