Introduction
In times of economic uncertainty, timely and accurate data is crucial for decision-makers. Traditional methods of estimating Gross Domestic Product (GDP) often lag behind real-world events, leaving policymakers with outdated information. Together with Prof. Kris Boudt, Prof. Koen Schoors, Feliciaan De Palmenaer, and Arno De Block from Ghent University, we have developed a novel approach to nowcasting GDP using bank transaction data, offering near-real-time insights into economic activity.
How It Works
Our approach utilises anonymised micro-level bank transaction data from both firms and consumers. By aggregating these transactions and mapping them to GDP components—output, expenditures, and income—we have developed indicators that provide a real-time snapshot of the economy.
Data and Methodology
The bank transaction data includes all types of financial transactions, such as payments for goods and services, wages, and investments. These transactions are categorized and aggregated to reflect the economic activities they represent. Our methodology involves:
Labeling Transactions: We tag each transaction with a specific label indicating its economic purpose, such as consumption or investment.
Aggregation: Transactions are aggregated on a weekly basis, creating high-frequency indicators that reflect economic activity.
Results
Our innovative nowcasting model has shown impressive results, particularly during periods of economic shocks. Here are some key findings:
Enhanced Accuracy During Shocks: Our model performed exceptionally well during economic crises, such as the COVID-19 pandemic, by quickly reflecting changes in economic activity. This allowed for more responsive policy decisions compared to traditional GDP estimates.
Close Alignment with National Bank Estimates: Our nowcasting indicators closely tracked the estimates provided by the National Bank of Belgium (NBB), validating the model's effectiveness. The correlations between the BNP Paribas Fortis transaction-based indicators and official GDP figures were high, demonstrating the model’s reliability. (See Fig 1. and Fig 2. below)
Timeliness: The real-time nature of the bank transaction data provided more timely insights than the traditional quarterly GDP reports. This is particularly valuable in the early stages of economic analysis when data from other sources is sparse.
Fig. 1 Aggregated weekly costs- and revenues-based BNP Paribas Fortis-UGent indicators vs the quarterly official GDP growth numbers. Note that the BNP Paribas Fortis-UGent indicators are produced weeks before the official numbers and at a higher frequency (weekly vs quarterly).
Fig. 2 BNP Paribas Fortis-UGent nowcast of GDP (red) vs the nowcast of the national bank (green and blue which are different models). In black the official GDP number. Note that the BNP Paribas-UGent indicator is produced weekly and thus comes out first out of all indicators shown, the final GDP number is published weeks after all others.
Implications and Future Work
The success of our nowcasting model has significant implications for economic policy and business decision-making. By providing near-real-time insights, policymakers can respond more swiftly to economic changes, potentially mitigating the impact of economic shocks. Businesses can also benefit from more accurate and timely data, aiding in strategic planning and risk management.
Limitations and Recommendations
While our model has shown great promise, it also has some limitations:
Data Coverage: Our model relies on data from bank customers, which may not represent the entire economy. We are working to expand data sources and improve representativeness.
Transaction Labels: Accurate labeling of transactions is crucial. We are continually improving the algorithms for categorising transactions to enhance the model’s accuracy.
Seasonal Adjustments: Adjusting for seasonal variations remains a challenge. Further refinement of these adjustments will improve the precision of the nowcasting indicators.
Conclusion
Our nowcasting model represents a significant advancement in economic forecasting. By leveraging real-time bank transaction data, we provide timely and accurate estimates of GDP growth, closely aligning with traditional estimates while offering the advantage of immediacy. As our model continues to evolve, it holds the potential to transform economic analysis and policy-making, ensuring that decision-makers have the most current data at their fingertips.