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A price index aggregates various combinations of base period prices (), later period prices (), base period quantities (), and later period quantities (). Price index numbers are usually defined either in terms of (actual or hypothetical) expenditures (expenditure = price * quantity) or as different weighted averages of price relatives ( p t ...
Hierarchical recurrent neural networks are useful in forecasting, helping to predict disaggregated inflation components of the consumer price index (CPI). The HRNN model leverages information from higher levels in the CPI hierarchy to enhance lower-level predictions.
A CPI is a statistical estimate constructed using the prices of a sample of representative items whose prices are collected periodically. Sub-indices and sub-sub-indices can be computed for different categories and sub-categories of goods and services, which are combined to produce the overall index with weights reflecting their shares in the total of the consumer expenditures covered by the ...
Consumer Price Index for Americans 62 years of age and older (R-CPI-E): This index re-weights prices from the CPI-U data to track spending for households with at least one consumer age 62 or older.
The Billion Prices Project (BPP) was an academic initiative at MIT Sloan and Harvard Business School that uses prices collected from hundreds of online retailers around the world on a daily basis to conduct research in macro and international economics and compute real-time inflation metrics. [1]
The Consumer Price Index was initiated during World War I, when rapid increases in prices, particularly in shipbuilding centers, made an index essential for calculating cost-of-living adjustments in wages. To provide appropriate weighting patterns for the index, it reflected the relative importance of goods and services purchased in 92 ...
A hedonic index is any price index which uses information from hedonic regression, which describes how product price could be explained by the product's characteristics.. Hedonic price indexes have proved to be very useful when applied to calculate price indices for information and communication products (e.g. personal computers) and housing, [1] because they can successfully mitigate problems ...
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]