Big Data application to predict macroeconomic indicators

Research p a p e r

BIG DATA APPLICATION TO PREDICT MACROECONOMIC INDICATORS TAGS-

Data collection, macroeconomic conditions, Big Data, Traditional Business Systems, The Internet of Things, Bayesian vector autoregression, VAR model in economics SERVICESResearch Planning | Data Collection | Semantic Annotation | Business Analytics | Bio Statistics | Econometrics

INTRODUCTION

Private agencies and government institutions are collecting and unifying information on several as pects of the economy, and over the period the opportunity of data collection has sufficiently grown, and therefore the quality of data has been enhanced.

Monitoring of macroeconomic condi tions has become the regular job of devoted economists at private institutions, banks, and government agencies, who scrutinize through big and complex data to refine all vital information. Copyright ÂŠ 2019 Statswrok. All rights reserved

BIG DATA SEARCH

Identification of Sources for Big Data An apt place for a calculation of the prospective welfares and expenses of the Big Data use for macroeconomic prediction is the identification of the source. Key source for big data are: 1. Social Networks 2. Traditional Business Systems 3. The Internet of Things

Social Networks

A foremost source is signified not only by human-sourced information otherwise known as Social Networks, which mainly explain to include social networking, but also e-mails, internet searches, comments, blogs, videos, pictures, etc. The allied data is roughly organized and frequently ungoverned.

Traditional Business Systems Big Dataâ&#x20AC;&#x2122;s second key source is process mediated data, otherwise called as Traditional Business Systems (TBS). These developments track and observe the interest of business events, like accepting an order, record keeping of a customer, manufacturing a product, etc. TBS data is the massive majority of what IT achieved and handled, in both business intelligence and operational systems. Generally, designed and deposited in database systems can be further assembled into data shaped by businesses and public agencies.

Internet of Things

The third source is the fast-expanding benefactor of Big Data, known as the Internet of Things (IoT). This data is derivate from machines which are used to track and calculate occasions and progress in the modern era. The concise way of data generated from the machine is apt for computer processing, but its size demands the usage of new statistical methodologies.

Interrelation Between the Sources of Big Data

Looking from the viewpoint of economic prediction, all these above mentioned three types of Big Data are theoretically related. For instance, selected social networks, IoT, TBS could all give relevant leading indicators for Gross Domestic Product growth of a nation. So, a vital step for the usage of Big Data for prediction is an arrangement of selected data, along with the features of the targeted variable.

Designing Big Data Strategy

Big Data can be implemented and categorized once it is temporally designed and well cleaned. This can implement several econometric procedures to equalize the target indicator with variables of Big Data. After the implementation, sample cross-validation of the substitute methodologies can be conducted. A conjoint method for the ventilated data is to either execute expectations or collect the data on the econometric models. Without any doubt, these expectations are not valid, and aggregation of data leads to a loss of data. So, Big Data econometrics is mandatory.

Framework for Monitoring Macroeconomic Indicators Using Big Data

There are two frameworks for monitoring Macroeconomic Indicators using Big Data: Bayesian vector autoregression (BVAR) Vector auto regressions (VARs)

Bayesian Vector Autoregression (BVAR) In real-time, for monitoring macroeconomy and prediction with big data, Bayesian vector autoreg ression (BVAR) deals with a substitute modeling framework. In BVARs, all the variables are independent when combined with this high level of complexity. BVARs are also applicable for prediction since they can perform in a space form letting for accessibly treating data with the help of filtering procedures. This is a significant way of study since Bayesian inference delivers a coherent model framework that can be misused to lessen the number and significance of subjective choices like the transformation of data.

Vector Auto Regressions (VARs) Vector auto regressions (VARs) are the most linear framework and are broadly used in macroeconomics. In VAR each and every variable hinge on its past and the outline of connection of the forecast faults in different variables is left unrestricted. VAR model in economics has already been backed by the primary exponents of these models. According to a recent study, itâ&#x20AC;&#x2122;s resulted that they are firmly allied with factor models and are appropriate for the scrutiny of big data.

CONCLUSION

Generally, we tend to assure that Big Data is appreciated in an exceeding nowcasting framework, not only to cut back the errors but to boost the suitability, occurrence of release and scope of data. The combination of the architecture in the present organizational systems is a perilous procedure to ensure the period of forecasts and nowcasts. The implementation of the frameworks in an apt cloud computing situation so that the operation can balance easily is also critical.

Future Scope

Strategy to implement the anticipated Big Data architecture to publish and create real-time predictions and forecasts of some macro-economic models using Internet data.

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