High-frequency and real-time indicators

December 10, 2021 0 admin

The pandemic-induced economic downturn of 2020 caused a global shift in the way the world tracks economic data. During the pandemic; economists, statisticians, and policymakers seeking to estimate the depth of the collapse in economic activity and the pace of recovery seized upon a new dashboard of previously obscure indicators. As investors and investment-bank analysts were eagerly waiting for the release of mobility statistics from tech companies (i.e., restaurant booking or card transactions, etc. in a manner once reserved for the official release of GDP and unemployment statistics), a new type of indicators suddenly came into limelight called high frequency (real-time) indicators (HFI).

HFI and the technology behind it

The word “high frequency” reflects data that is collected at an extremely fine scale as a result of advanced computational power.

Such real-time data, which is delivered immediately after collection in a large amount maintaining statistical precision, is usually processed using a computer system called “real-time computing (RTC)”. The system must guarantee a response within specified time constraints (“deadlines”, generally at the millisecond level), regardless of system load.

Several methods exist to aid the design of real-time systems, which include MASCOT, HOOD, Real-Time UML, AADL, the Ravenscar profile, and Real-Time Java.

Global hunt for latest-health data during the covid crisis to make timely decisions

The importance of data in decision-making cannot be overemphasized. More so during the pandemic when world leaders had to make crucial policy decisions every day to contain the spread of the virus and to mitigate its economic impact. To make important decisions on issues like extending lockdowns or fulfilling hospital demands (need for more ventilators etc.) that have a direct bearing on lives and livelihoods, governments need real-time and more frequently updated data.

Realizing the stringent need for real-time health data, various countries have launched their COVID-19 apps for contact tracing and vaccinations. These apps are also assisting covid patients in getting real-time hospital bed vacancy data to make an informed decision during the critical time.

HFI’s “timeliness” is attracting the attention

Also, for policymakers and investors, the main attraction of real-time indicators is timeliness, as the datasets are continuously updated in real-time. This allows analysts to improve their understanding of what is happening now in the areas most relevant to them, and to tailor their interventions to be as effective as possible.

On the other side, official data (also called hard data), such as inflation, employment, or GDP, tend to be released with a lag of several weeks (or even months, given the complexity in collecting and computing them) at a time when, perhaps, the world moved on to a different phase.

The appetite for real-time indicators became so strong in the last year that some of the leading market regulators started using new econometric modeling techniques to integrate continuously flowing information into their forecast models. This includes India’s central bank, The Reserve Bank of India (RBI), which – in its maiden attempt – started constructing a regional “Economic Activity Index” from November 2020 that incorporates 27 high-frequency indicators to gauge the dynamics of growth and output. UK’s Office for National Statistics (ONS) is also working on a proposal to produce its coincident indicator of real-time economic activity, similar to the United States (New York) Weekly Economic Index which was initiated only in March 2020. This comes after the UK published the first such indicators in April 2019 (just to track the Brexit effects) that played a key role in informing policymakers during the rapidly changing times.

Here are examples of some of the indicators ONS has considered to track the effectiveness of social restrictions on the economy.

High-frequency indicator Source
Retail footfall Springboard
Road traffic The Department for Transport
Restaurant reservations OpenTable
Job vacancies Adzuna
Card transactions CHAPS payment data provided by the Bank of England

The table below reflects how some of the other large economies forayed into their own set of experiences to track real-time data.

Country Remarks
Germany ·         The Federal Statistical Office publishes daily mobility indicators.

·         Monthly truck-toll-mileage index indicates the development of industrial production.

France ·         The National Institute of Statistics and Economic Studies publishes daily bank card transactions, daily road traffic data, weekly Google mobility figures, and daily data on electricity consumption by residential and non-residential customers.
United States ·         The Bureau of Economic Analysis publishes daily card transactions.

·         the US Census Bureau publishes Household Pulse Survey (to track employment status, spending patterns, and travel practices) and Small Business Pulse Survey (to track activities of small businesses) every week.

Examples of real-time indicators

Global turmoil of 2020 resulted in the broadening of the set of HFI tracking the economic activity. This allowed looking for as much closer to real-time information to understand what types of activity have been affected.

For example, social restrictions announced during the lockdown periods led to an interest in tracking mobility indicators to understand their impact on the economy. The chart below explains how the sharp fall in mobility level esp. during the first lockdown (April 2020) in the UK indicated deterioration in domestic economic activities.

UK: Google Mobility and Monthly GDP index UK: credit and debit card purchases (A backward-looking seven-day rolling average, from 3 February 2020 to 30 April 2021)

Source: Office for National Statistics, Google                                                             Source: Office for National Statistics and Bank of England calculations

Similarly, card transactions data indicate the underlying trends of consumer spending as these directly capture the economic transactions that are of interest. In the above chart, credit and debit card transactions data sourced from Bank of England’s CHAPS payment system also shows trends in the compositional spending based on behavioral responses to social restrictions with respect to its severity. This may also help to track whether these trends will prove to be temporary or permanent in the future.

With the growing use of the internet and digital technologies across the globe, the tech giants could also provide information on trends related to customer demands. One such example is Google Trends, which tracks what people are searching in the search engine and how the search term queried is changing over time. For example, if a sudden increase in searches for tourist spots in an area is detected, it could indicate an upswing in tourism and economic activity in that region, enabling a company to increase targeted marketing spend.

The challenges of HFI

Although still in its infancy, the real-time indicators possess unique challenges concerning their quality and accessibility.

Some of these indicators, having unusually smaller sample sizes and excessive volatility, may present an unreal picture, unlike the traditional indicators which are constructed with economic and statistical expertise. These indicators may only be representative of the geographies/cities they are generated from and cannot be taken as a proxy for national indicators of economic activity.

Accessibility is another challenge as several of this real-time data is being produced by private research institutions, and may not be freely accessible on demand.

Also, concerning the UK case discussed above, the mobility indicator appears to be less correlated with economic activities during the subsequent lockdowns (esp. on November 2020 and January 2021), where the fall in output was much less pronounced than what mobility levels would have implied, as the businesses have adapted their behaviors to the impact of pandemic over time. This highlights the challenges of these types of indicators that are indirect proxies for spending, as these relationships can change at any time.


The depth of the downturns induced by covid-19 has put a premium on swift intelligence. This reflects a shifting focus towards continuously flowing data to identify the current state of the economy. As per the World Bank article, some underlying indicators such as the Google mobility indicators—which measure the visits to workplace, retail, etc.—proved to be very important predictors in 2020.

Also, the growing interest from some of the global market regulators to foray into this trend and make their own aggregate real-time indicator (like a regional economic index) will improve HFI’s accessibility and overcome the challenges.

Thus, the real-time information can help decision-makers in localized understanding of problems to conduct a course correction by channelizing resources more efficiently and ultimately creating a roadmap to economic recovery.