Thierry Kalisa started working with new data for real-time economic projections, or “nowcasting,” a decade ago, but the pandemic brought its potential into sharper focus.
As a Rwandan finance ministry official when COVID hit Kigali, the capital, he teamed up with a joint task force with the central bank to monitor a shuttering economy under sub-Saharan Africa’s first lockdown. Official economic indicators would soon be outdated, even before publication.
The group launched a weekly economic activity index based on a central bank measure incorporating factors like exports, imports, and real-time consumer spending captured from the tax authority’s electronic billing machines in stores. The outlook deteriorated. The economy would soon contract.
“This helped the government revise growth projections, adjust the macro framework, and take timely policy actions,” said Kalisa, who joined the central bank as chief economist in 2021.
The National Bank of Rwanda today includes nowcasting in staff briefings before quarterly Monetary Policy Committee meetings, and Kalisa’s staff of economists, statisticians, and data scientists is expanding to help deliver. “This is very demanding in terms of analytical capacity, but is also producing high-frequency indicators,” he said.
Data gaps
Rwanda is among developing economies taking a new approach to economic measurement. Many aim to narrow gaps with advanced economies and most emerging markets in official indicators many developing economies publish infrequently or with a delay. Those advanced and emerging market economies have the staff, funding, and other necessary resources. Large populations in developing economies, however, are left out.
Initiatives include real-time trackers of economic growth, inflation, trade, and consumption. Several low-income countries are building out data operations with support from IMF capacity development and technical assistance (see sidebar).
Data gaps affect low-income countries disproportionately. Advanced economies and most emerging markets publish GDP quarterly. But about a third of countries in the world have only annual GDP, leaving policymakers in the dark for long periods.
And GDP, even for the countries that publish it quickly, still comes out a month or more after the end of the quarter. During crises, the wait bedevils policymakers, who must steer the economy without knowing which way it’s heading.
The unprecedented disruption of the pandemic drove this reality home and pointed to the need for more timely and frequent measures to complement official data. Some activities ceased, others exploded, and indicator data collection suffered, causing distortions. Bruno Tissot, head of statistics at the Bank for International Settlements and secretary of its Irving Fisher Committee on Central Bank Statistics, calls it “statistical darkness.”
“Central banks around the world have recognized the primacy of providing timely indicators, for instance by mobilizing alternative high-frequency data sources, constructing weekly or even daily indicators, and enhancing their nowcasting exercises,” the committee observed in a 2023 report on postpandemic central bank statistics.
Forecasting tool
Nowcasting originated in the 1980s as a meteorological term for predicting conditions just a few hours ahead. It means something else in the world of economics.
“With the weather, you look outside of your window, you see whether it’s raining or not,” said Domenico Giannone, a nowcasting pioneer. “In economics, you need to wait.”
Giannone’s 2008 paper on nowcasting, coauthored with Lucrezia Reichlin and David Small, is credited with introducing the term to economics.
Giannone and Reichlin, then at the Université Libre de Bruxelles, began developing a model for short-term forecasting in 2002, in response to a request from Ben Bernanke, one of the Federal Reserve governors at the time. He asked them to explore the feasibility of a comprehensive big data model for forecasting and policy analysis, covering interactions among key sectors of the economy. Giannone and Reichlin discovered that prediction was possible only for the present, very recent past, and very near future—what they labeled “nowcasting”—and built a model to use real-time data to do so. It put what was previously informal, and based largely on judgment, into a formal statistical framework.
“The Fed was interested in seeing whether that kind of framework could be adapted to the problem of reading all the different releases in real time,” recalled Reichlin, a professor at the London Business School and former research director at the European Central Bank (ECB). “At the time, macroeconomic models were relatively small—it was before ‘big data’—and we started thinking, What models could handle a lot of time series and at the same time retain some simplicity so as not to generate volatile and unreliable estimates?”
Giannone later built on that work at the New York Fed, where he led development of a nowcast of weekly estimates of quarterly US economic growth.
After roles at the ECB, Amazon, and the IMF, Giannone joined Johns Hopkins University this year to focus on improving economic activity measurement in low-income countries. He was motivated partly, he said, by the realization that nowcasting tools of larger, wealthier economies covered nearly the entire global economy, but low-income countries had almost nothing.
Flying blind
Low-income countries face challenges with nowcasting and with the official data it complements, especially when government budgets are strained and skilled staff scarce. But practitioners still see promise in sharpening real-time measures.
First estimates of GDP in many advanced economies come out about a month after the end of the quarter—two months in some major emerging market economies—then are revised. In developing economies it can take more than three months.
Kenya’s National Bureau of Statistics, for example, releases GDP about three months after a quarter ends, but the central bank uses nowcasting tools fine-tuned by IMF staff and Giannone to start gauging the quarter after only a week, using private consumer spending data, then remittance data available after two weeks. Trade, money supply, tourism, and electricity data available in about 40 days help refine the picture and give a good indication of the health of the quarter in half the usual time.
