Research paper
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
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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 conditions 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.
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BIG DATA SEARCH
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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
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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.
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Traditional Business Systems
Big Data’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.
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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.
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Interrelation Between the
Sources of Big Data
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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.
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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.
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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)
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Bayesian Vector Autoregression (BVAR)
In real-time, for monitoring macroeconomy and prediction with big data, Bayesian vector autoregression
(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.
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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, it’s resulted that they are firmly allied with factor models and are appropriate
for the scrutiny of big data.
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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.
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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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