Please use this identifier to cite or link to this item:
https://repository.iimb.ac.in/handle/2074/13491
DC Field | Value | Language |
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dc.contributor.author | Ghosh, Pulak | |
dc.contributor.author | Ghosh, Soumya Kanti | |
dc.date.accessioned | 2020-07-20T14:37:33Z | - |
dc.date.available | 2020-07-20T14:37:33Z | - |
dc.date.issued | 2015-05-27 | |
dc.identifier.uri | https://repository.iimb.ac.in/handle/2074/13491 | - |
dc.description | The Economic Times, 27-05-2015 | |
dc.description.abstract | Pick up the analyst interaction (even the Technical Advisory Committee minutes) with the Reserve Bank of India governor and you will find that after every policy announcement, there is at least one question regarding consumer inflationary expectations. Herein lies the role of ‘Big Data’ to unravel the mystery of correctly deciphering such expectations. Technically, Big Data refers to large volumes of structured and unstructured data and can be a combination of digital information of both, derived from the consumer interaction in the digital world, like web applications, social networks, sensors, etc, that can be used in facilitating policy decisions for banks (central banks included). The Billion Prices Project (BPP) at the Massachusetts Institute of Technology, led by professor Alberto Cavallo, actually took the internet’s help to gather inflation data from online stores. Cavallo wrote a computer programme that scanned websites’ HTML codes, and found out all the prices of online goods. The estimates through the internet very closely matched the ones produced by statistical offices of the US. The project now gives prices of about half-a-million products per day and while the Government Bureau in the US comes out with monthly inflation numbers, the BPP gives the daily inflation rate. Imagine the RBI using similar algorithms to scan retail inflation data on a daily basis (published by the ministry of consumer affairs) as well as weekly (Directorate of Economics & Statistics) and juxtaposing it with data from even supermarkets to generate an inflationary expectation index on a daily basis in advance. Such online price data will be of very high frequency, available almost in real time. It will have detailed information on product consumption and will thereby give greatly improved forecasting abilities to the RBI. Read more at: https://economictimes.indiatimes.com/blogs/et-commentary/how-big-data-can-help-central-banks-with-real-time-policy-analytics/ | |
dc.language.iso | en_US | |
dc.publisher | Bennett, Coleman & Co. Ltd. | |
dc.subject | Banking | |
dc.subject | Financial management | |
dc.subject | Monetary policy | |
dc.subject | Economic policy | |
dc.title | How big data can help central banks with real-time policy analytics | |
dc.type | Magazine and Newspaper Article | |
dc.identifier.url | https://economictimes.indiatimes.com/blogs/et-commentary/how-big-data-can-help-central-banks-with-real-time-policy-analytics/ | |
dc.journal.name | The Economic Times | |
Appears in Collections: | 2010-2019 |
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