ecomomics data api
Utilizing an Economics Data API for Informed Decision-Making
When analyzing economic data, the economist follows mapping goals to explain the observed data well. Economic practices emphasize theory and model realism. Careful data management ensures reliable model tests. Optimal economic research learns from database-related estimators.
Summarizing an extensive database requires a systematic approach to extract decision-making information. Utilizing economic decision-making techniques and recognizing patterns in economic data cultivates informed decisions. Broad economic datasets drive accurate conclusions. The guiding economic principle suggests that applicable data components continue to change.
Economies require high-quality, relevant information to form both macro and micro-macro judgments. Researchers take raw data series and transform them into usable statistics by applying statistical procedures. This is generally done by collecting economic data that are then packaged into structured datasets. The raw data and the procedures used to harmonize data differ from provider to provider. To choose a representative dataset, a user must consider the merits of various procedures. For example, a user may opt for a provider whose premises and perform harmonization procedures such as detailed corrections, which prioritize the concept of purchasing power parity, to deal with relative price differentials before combining local price series to create international series. Ultimately, selecting the most appropriate dataset to use in an investigation is a significant hurdle faced by various user communities.
The Open Economics Dataset is a language-design, which aims to utilize the wealth of economic information from existing and new resources. OED resources provide access to various information types, including overviews of available economic data resources, types of data provided, descriptive statistics, search functions, and application program interfaces (APIs). The goal was to act as a resource that could, in turn, be utilized by researchers to build analytical tools that could benefit broader science and society. In this document, we discuss the process of building one such resource, the Open Economics Data.
Access to accurate, timely, and comprehensive economic data is essential for making and evaluating informed decisions and analyses. The majority of activities can benefit from more data and a better understanding of these types of datasets. For instance, policymaking, business and investment decisions, public opinion monitoring, and education can all become more productive with this information. As a result, data – as the subject of research, policy initiatives, and as a production factor – are a substantial part of the evolving knowledge-based global economy. An increase in availability, accessibility, and utilization of these types of datasets can result in effective economies and societies.
Understanding the economy, and the associated economics data, can be achieved more easily when companies embrace modern technology, data visualization, and statistical methodologies when making business decisions. MathWorks offers the Economics Data API which focuses on both macroeconomic and industry economic indicators. The MATLAB-based interface gives companies a simplified access to the data, and the methodologies, through an easy-to-use interface. The underlying two-way interaction with the Economics Data API harnesses some of today’s best technology by utilizing MathWorks’ data analytics and machine learning capabilities as well as providing a tailored environment suited to the needs of domain experts and data scientists.
As the world continues to globalize and information technology continues to become more accessible to people in every part of it, business decisions are often made based on world economic data. With how interconnected world economies are, what happens in one country impacts markets in other countries. It is now more important than ever to understand economics when making business decisions in order to stay competitive. While world economic events can shape the length of economic cycles, as well as corporate profitability, effective and responsible management can help ensure that businesses continue to be able to meet and exceed market expectations and contribute to needed innovations. Companies must also prepare for future competition in a time of continuous business disruption and ongoing fluctuations in world economic activity. In order to stay competitive, businesses should strive to understand the economies in which they operate and take steps toward proactively shaping the world economic environment.
In this way, an API has the potential to serve any technology-savvy individual or organization capable of tapping into the steward’s services very quickly, making the steward as effective as Spring in rushing you with results. This is a simplified version of the process, of course, and we’ll spend the rest of this chapter discussing APIs – what they are; what makes a good API; who are their most constant and heavy users; and why they are essential as a resource for making informed decisions. Finally, we will compare the IEO’s webSTATS to a cloud-based API service and list many of the economic databases and tools that you can find without any charges.
Are you interested in information regarding international exchange rates or purchase-power-parity data? Just query the statistics group of the International Monetary Fund and they will give you all that you need. Or, if you prefer, ask the International Organization of Securities Commissions for over-the-counter derivatives reports or risk metrics regarding central counterparties. And this all happens within moments, like magic!
When you request a certain data set or function to be executed, the intermediary will speak to the appropriate steward who will find and retrieve the tool you requested. A whole bevy of stewards are waiting inside for your query, seeing the API as a virtual hotel concierge who tells them, “Our customers request from me: Now, how may I help you?”
There is an API for nearly everything you can think of in the digital world – news articles, weather updates, maps, microblog social media data, and of course, economic data. An API, the shortened form of Application Programming Interface, provides ways for many different software interfaces to communicate with one another. Most often, APIs serve as an intermediary between your computer and a cloud-based server. Think of this cloud server as a great iron fortress housing an unimaginable sum of toolboxes, each filled with a particular data set, spreadsheet, function, or microservices.
For example, global exchange rate data is useful due to direct and indirect trading relationships that exist involving the NSE and international financial markets. Obtaining economics data manually is not practical because of its volume, diversity, and its frequent publishing. The COVID-19 pandemic has shown the importance of monitoring economic indicators closely. This study presents a proof-of-concept on how to develop an economics data application programming interface and how it can be used in the financial markets with the NSE as a case study.
The Nairobi Securities Exchange (NSE), Kenya’s primary financial market platform, uses economics data to make informed investment decisions. The Economics Data Application Programming Interface (API) enables users to programmatically retrieve select structured economic databases from the European Central Bank (ECB) servers using standard SDMX codes. Economics databases such as interest rates, economic term structures, stock price market information, and global exchange rates are of value to investors in the NSE.
Getting real-world economics data is one way by which economists and interested parties make informed economic decision-making in the fields of research, finance, business, and investments. As information is key in making informed decisions, data becomes the foundation of informed decision-making. Governments of different world economies are the primary source of economics data. Their economic data is published on their respective digital platforms such as websites in a structured data format using standards such as General Data Protection Regulation (GDPR) and Statistics Data and Metadata Reporting (SDMX).
An immense amount of time and effort is spent on consumption of data. APIs can automate the entire process, providing access to incredible economics datasets with a single request. The investment made by the company in the development, maintenance, and operations of an API grants the developer’s team realizable benefits that outweigh the investment and provide economies of scale for any would-be consumers desiring access to the economics dataset. By investing those resources in building and bringing to market new economics data, additional capabilities and tools that are relevant to the real-world problems of definitive companies—if take advantage of the API investment principal companies can improve productivity, conserve business funds, and accelerate “time-to-value” for data-consuming objectives. Given the increasing data componentry of modern programming tools, an economics data API meets the needs of developers that are growing self-sufficient and capable. Offering required economic data where developers create assets will assure proper utilization of the API and a functionally improved product.
In information-driven societies, data-driven decisions are essential. This paradigm shift in many domains underscores the importance of availability and utility of data. One such domain is economics, where financial policies influencing the scale of crucial infrastructures, exports and imports, etc. are taken based on predictions and analysis of economic data. Parsing through, validating, and then making judicious use of the plethora of data available online is a tedious and time-consuming process. This could also lead to missed opportunities due to potentially outdated data used for decision-making. One possible solution is to use an open and commercial API powered by underlying tools that simplify access to enormous amounts of real-time economics data. This paper discusses briefly about Aspects of Economics Data by covering the current state of utilizing an Economics Data API which make teams more productive, minimize time-to-deployment, provide better access, and make content work in the best possible way.
In the development of efficient applications that are going to help people in their economic decision-making process, APIs are of great use. People are looking for new innovative solutions because most websites do not include a medium that brings these together in a correlated and personalized environment. By providing an easy access service of historical and real-time economic data and other relevant news, users expecting to contribute to the improvement of less financially literate and low-income users. Through the aforementioned analysis, we conclude that personalized financial recommendations based on real-time and historical economic data, personalized financial recommendation news as well as financial aid recommendation news are necessary services that provide smart decision-making and it could be included within a new innovative financial portal that facilitates the access to historical, models-based and real-time data optimizing without restricting the user interface or user experience.
When making use of external data, accessibility is often the important thing to consider in decision-making, and data are important for measuring one’s decision or making. The first step towards better decision-making includes improved informational access. Timely and accurate information make up the core of smart decision-making. This access is provided by different institutions, including libraries, governments, weather reports, and paid financial data service applications. The internet is the most important source of up-to-date economic data. There are a variety of websites that offer up-to-date news, report real-time data, and facilitate the exchange but very few websites provide an all-around architectural environment that aims to rapidly and intuitively enhance economic decision-making.
However, beyond the focused analytics platforms on the respective secondary resources, modeling and testing the relationships of economic phenomena through a myriad of related dimensions with statistical software has not yet evolved. To define the relationships, visualization of trends and patterns in data should be undertaken through an in-depth analysis of large-scale dimensionality reductions, rigorous self-organized maps, and unsupervised learning methodologies. Order-parameter-based data structures and statistical methods also help to computationally observe macroscopic diagrams. Here, although some data might be of interest as part of a statistical learning and data mining exercise, the actual integration required for the abundance of relevant data to be combined in one platform devoted to economic science for collective reasoning is yet to be explored. Efficient methods and algorithms that provide quicker, more accurate solutions while processing less data are also crucial in big data machine learning and artificial networks.
Data analysis and visualization in decision sciences play a key role in generating insights and guiding the decision-making process. For example, descriptive analysis categorizes the general trends and patterns of the data. As such, analysts gain a better understanding of the focal construct prior to modeling relationships. Given its capacity to define explanatory or predictive patterns of variables, test hypotheses, and predict unknown values, statistical analysis has been considered the most attractive tool for data analysis in decision sciences. Its importance can be reflected in the requirements of CAMDA (The Critical Assessment of Microarray Data Analysis) prediction, where different stages of statistical analysis are indispensable components. By means of statistical analysis, analysts are able to construct meaningful patterns, which could not be easily identified via visual inspection. This underscores the significance of statistical analysis to identify structures or relationships among data features/tools that are not discernible in visual exploratory data techniques.
Statistical prediction models are often disparaged, claiming the primary source of value will come from decision-making discipline and superior data rather than better data analysis. To some extent, there remains an element of truth on this topic, particularly when the decision hinges on future value, as even perfectly accurate forecasts would not yield the desired result. The assertion from researchers regarding economic and financial data has been that even if statistical models did not have any predictive power, the reconciliation of model estimates with the data would yield substantial promise for informed decision-making. From this notion, the data predominantly blamed the absence of influence from standard theory-based models, which ironically can still result in huge forecast errors.
A major advantage in performing data analysis is that financial or economic data is quite helpful for decision-making and forecasting due to the availability of past information. Forex trading, for example, is enormously data-driven as analysis of trends can provide behavioral clues, weighing predictions on economic reports and other news. Once the data is spoon-fed into a machine learning model, this can automatically initiate trades based on the outcomes from the trained model. In recent years, many researchers have utilized machine learning methods specifically by injecting more statistical techniques where in most situations, prediction performance was superior to standard model fit.
This feedback can be used to guide policy formation to help the community and the state. Businesses can use those tools to help formulate strategic directions such as expansion to an area when the GDP by a County: NAICS-based GDP by industry statistics, May 15, 2018 can be shown. Moreover, the data can be used to demonstrate to policy makers the local areas seeing economic disruptions or incite real estate update when GDP in that year is greatly falling in the county. The economic statistics can provide an at-a-glance snapshot of the state or county’s economic health. In this work, we demonstrate how to utilize an Economics Data API as a foundation by implementing the Economic and Demographics datasets into an interactive self-updating data visualization tool for Economics and ultimately help in informed decision-making by policy planners, academics, businesses, non-profits, and the general public.
Our data visualization tool can be used as a decision-making tool to help policy makers and businesses form and implement the appropriate economic policies. In contrast to traditional economic data search tools that provide manually updated data and display tools to help users analyze the data on the web, our data visualization tool is self-updating, automated, and is built upon the request from the user during the presentation for relevant real-time data. Utilizing the same COVID-19 data from the deaths and vaccination datasets, a simple COVID-19 analysis chart can be created and configured on the Graphical User Interface. In this section, we discuss the implementation of the Economic Data set from our solution. Due to space limitations, we present a simple time-series data from our Census-searched “Real GDP by State: All industry total, 2012–2021 and Nominal GDP by state: All industry totals, 2012-2021”. The economic concept behind this GDP visualization tool is to show public workers the current economic trends in their county compared to a state’s coefficient.
To further the success of this JavaScript choice, and to lessen the overall development time, a high-level JavaScript library called Axios was included. Axios can be included within almost any JavaScript project without the requirement of a pre-processor or transpiler, and even supports older web browsers such as Internet Explorer 11, which is important because the department does not enable the usage of alternate browsers. Beacon and Butterworth. To extract the most value from the returned data, requests for multiple series for a single state can be submitted within the same transaction. These requests can be appended to an array which will then be included as the data property of the single JavaScript object returned by the Axios Promise.all function. If an error occurs in the submission of a request, the error message can be captured in a catch block surrounding the Promise.all function.
Since the API is based on REST principles, it can be accessed by any application or language that has the ability to make HTTP requests. This enables limitless possibilities for integration with existing systems. With the goal of long-term private departmental investment in mind, the decision was made to utilize JavaScript to access the API due to the ongoing business transformation of other existing systems. One of the main reasons for this JavaScript choice was the ability to utilize SharePoint to disseminate the application to all users within the department with very little developer time investment.
In our API, we applied a continuous integration strategy, which allows us to automate quality control aspects by effectively constructing niche business objectives. The utilization of advanced infrastructure and continuous enhancement of the code quality are critical components of the project. By applying continuous integration, we have successfully maintained the system code quality. In particular, we established code quality measurements that guided us through the automatic identification of issues and vulnerabilities. Lastly, appropriate coding techniques have been implemented to negate well-known vulnerabilities, and as a result, specifications have been met.
A key consideration is that sensitive data is anonymized, and the API’s usage and handling of personal information comply with internal guidelines and privacy regulations, such as the General Data Protection Regulation (GDPR) in Europe. The API communicates the data using JSON files on the web for data distribution and dissemination. It is very important that all data is thoroughly read and verified. The API follows the communication process and completes the associated method, such as GET. Once the backend processing is completed, an extension signals that the GET is finished. During the API workflow, all callbacks must be preserved for safe recalling while JSON files undergo further processing. Ensuring that errors are minimized is a priority for data protection. Furthermore, after completion of all the API’s operations, appropriate handling of raw data should be initiated to the database by correctly formatted JSON files.
Security and privacy are paramount for transmitting personal and sensitive data, along with financial information. Ensuring that data privacy is maintained is a responsibility that our company takes seriously. The API uses solely HTTPS to access the resources, where the TLS v1.2 and Ingress Protection protocol is the minimum allowed security level. In addition to providing an encrypted connection, the API services perform a safe form of authentication through an address bar lock or secure padlock icon. Security labels, such as “HTTPS,” are visible for clients.
Providing only one concentration area in a degree can stunt the professional development of graduates. Economics graduates are no exception. In addition to economic theory and calculus skills, economics students benefit by having supplementary concentrations. For instance, a concentration in big data allows the student to utilize large datasets to test the tenets of economic theory. A concentration in analytics allows the student to utilize the latest computational methods to create informative analytics to inform decision-making. Educational programs, especially those that only concentrate on one skillset, frequently confound analytical skills with technical skills and do not teach the student about the distinction. By having a concentration in analytics as opposed to a concentration in a technical area, such as R or Python, the student is encouraged to look at a wide array of computational methods which will allow the student to create informative analytics regardless of the programming language used.
As data professionals, we have an obligation to inform decision-makers. We have learned of the myriad of means through which we may inform. From simple visualizations to rigorous inferential statistical analyses, the tools that we have at our disposal to distill information, not knowledge or wisdom, from the data that we collect are broad and growing. Our discipline is not the sole repository of knowledge; however, we hold a special role in that we are the only ones at a decision-making table who possess the skills and full access to the means to perform analyses or inspire others outside of our discipline to perform them. We can use our economic data to inform decision-makers because we are the connection between technical expertise and decision-making.
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