{"id":20534,"date":"2019-09-19T11:33:22","date_gmt":"2019-09-19T10:33:22","guid":{"rendered":"https:\/\/blog.frankfurt-school.de\/?p=20534"},"modified":"2020-05-18T21:19:23","modified_gmt":"2020-05-18T20:19:23","slug":"nowcasting-inflation-machine-learning-to-resolve-the-classification-challenge","status":"publish","type":"post","link":"https:\/\/blog.frankfurt-school.de\/de\/nowcasting-inflation-machine-learning-to-resolve-the-classification-challenge\/","title":{"rendered":"Nowcasting Inflation: Machine Learning to resolve the classification challenge?"},"content":{"rendered":"

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Understanding inflation dynamics is crucial for the conduct of monetary policy. However, official inflation statistics are published only with a time lag. Hence, central bankers are interested in \u201cnowcasting\u201d inflation, among other variables. Nowadays we have data on prices of individual products posted every day on the internet. In order to do nowcasting, we need a mapping between any particular product and the official statistical classification: is \u201cchocolate chip cookie ice-cream\u201d a baked good or a refrigerated dessert? Web-scraped price data have been studied by others like in the Billion Prices Project<\/a>, but nobody has yet produced a detailed mapping from individual products to the statistical classification. The question is: How to categorise millions of products into standard statistical categories that require expert knowledge for classification?<\/p>\n

The classification challenge<\/h2>\n

The classification scheme for products that we use is COICOP (Classification of Individual Consumption According to Purpose<\/a>), which is published by the United Nations Statistics Division and enhanced nationally.<\/p>\n

COICOP has over 300 (sub)-categories. Each category is described only textually.<\/p>\n