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In the global economy, information and internet technology has boosted current web-based services that change every aspect of today’s financial and economic activity. A new way of immersing this processed information into various research fields is to use Big Data. Because of its convenience and recent developments is widely exploited by numerous fields other than economy such as politics, healthcare, management, engineering. Not surprisingly, since the Financial Crisis in 2008, economic research done on Big Data has considerably increased. What is more exciting is that in 2013, it was assessed that 90% of all data ever created arose in the past those two years.
Therefore, we come across a pile of information that we do not exactly know how to handle. So, I want to conduct my research on how central banks should integrate their models with the new insights and methodologies we gather from Big Data. In my dissertation, I plan to investigate ways of integrating Big Data and central banks’ policies in a way that each one of them can use or adapt to better forecast the sentiment of individuals and firms, and deepening our understanding of the economy and financial system.
Moreover, my goal is to gather information from Big Data to study financial crises to find the preliminary signs. Hence, my research question would be how central banks could be involved in Big Data methods, statistics algorithms to isolate supplementary signals and insights from each aspect of the economy.
The experience of Big Data will possibly fortify examination of decision-making, by contributing increasingly complete, quick and granular data as a supplement to traditional macroeconomic indexes.
Throughout, various methods are being grown, frequently named to as “artificial intelligence” and “big data analytics”. These assure quicker, progressively comprehensive and expanding associated bits of knowledge, as contrasted to traditional analysis and approaches. An expanding number of national banks have propelled explicit Big Data to take actions to investigate these subjects.
The following points of this new approach is generally discussed under the its many domains. Big Data provides new sorts of information source that supplement to the traditional assortments of insights. These sources such as social media compromise of Google searchers, consumption goods and real estate prices costs showed on the web, and indicators of individuals’ and firms’ attitudes and expectations. As a result of information technology advancement, new methods can be utilized for gathering information (web-scraping), processing textual data (text-mining), coordinating various information sources (fuzzy matching), isolating related data (machine learning) and presenting appropriate indexes (interactive dashboards).
Specifically, big data methods, for example, decision trees may shed fascinating light on the decision-making mechanism of agents in the economy – consumers, firms, banks, investors – as selling and buying, producing, strategizing, and forecasting. As an additional example, indicators of monetary uncertainties taken out from news stories, may help clarify changes of macroeconomic indicators. This delineates big data’s possible power on giving information not only into what occurred, but also into what might occur and why.
There is solid intrigued in big data within the central banking community but central banks actual inclusion within the use of big data is currently constrained. Numerous central banks are as of now utilizing big data collections for macroeconomic estimation. Certainly, nowcasting applications as portrayed above can be viewed as a particular sort of estimation exercise. For example, the study by Per Nymand-Andersen indicated how Google Trends information can be utilized to aggregate transient projections of assessments of car sales in the euro region, with a lead time of a little while over real production dates. Additionally, and as mentioned in the study by Alberto Urtasun big data enables a more extensive scope of signals to be utilized for predicting main indicators – for example Google Trends, uncertainty measures or credit card activities together with more traditional indicators.
Although, there are many unseen problems in the details, the studies try to attempt a few methodologies. For example, many indicators may function admirably in nowcasting GDP (growth rate of a quarter over the present quarter) yet less so in anticipating its future development (growth rate one year ahead). Another point is that the web isn’t the sole root of indicators that can be utilized in this situation; actually, many online indicators may work less well in forecasting and nowcasting than do traditional confidence index.
Investigating big data is a complex, multifaceted assignment and standard generation of big data-based information will take time, especially because of source issues. To begin with of all, I will select few cases considers for guiding the usefulness of “big data”, as a possibly compelling tool-kit, assisting in financial stability, central banks’ monetary policy, and banking supervision approaches. These supplementary measurements may give more bits of knowledge in supporting to guiding central bankers’ approach as well as to surveying the successive effects and related dangers of these approach choices on the real economy and financial framework. The way forward will be to require advance steps in creating and applying a structural approach for guiding the usage of big data, for central banking objectives.
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