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Using National Payment System Data to Nowcast Economic Activity in Azerbaijan

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Abstract

This study examines whether payment system data can be useful for tracking economic activity in Azerbaijan. We utilise the transactional payment system data at the sectoral level and employ a Dynamic Factor Model (DFM) and Machine Learning (ML) techniques to nowcast quarterover- quarter and year-over-year nominal gross domestic product. We compared the nowcasting performance of these models against the benchmark model in terms of the out-of-sample root mean square error at three different horizons during the quarter. The results suggest that ML and DFM models have higher predictability than the benchmark model and can significantly lower nowcast errors. Although our payment time series is still too short to obtain statistically robust results, the findings indicate that variables at a higher frequency in such data can be helpful in assessing the current state of the economy and have the potential to provide a faster estimate of the economic activity.

Suggested Citation

  • Ilkin Huseynov & Nazrin Ramazanova & Hikmat Valirzayev, 2022. "Using National Payment System Data to Nowcast Economic Activity in Azerbaijan," IHEID Working Papers 23-2022, Economics Section, The Graduate Institute of International Studies.
    • Handle: RePEc:gii:giihei:heidwp23-2022
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    References listed on IDEAS

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    1. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008. "Nowcasting: The real-time informational content of macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 665-676, May.
    2. Valentina Aprigliano & Guerino Ardizzi & Libero Monteforte, 2019. "Using Payment System Data to Forecast Economic Activity," International Journal of Central Banking, International Journal of Central Banking, vol. 15(4), pages 55-80, October.
    3. Kohei Maehashi & Mototsugu Shintani, 2020. "Macroeconomic Forecasting Using Factor Models and Machine Learning: An Application to Japan," CIRJE F-Series CIRJE-F-1146, CIRJE, Faculty of Economics, University of Tokyo.
    4. Marta Bańbura & Michele Modugno, 2014. "Maximum Likelihood Estimation Of Factor Models On Datasets With Arbitrary Pattern Of Missing Data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(1), pages 133-160, January.
    5. Reichlin, Lucrezia & Giannone, Domenico & Small, David, 2005. "Nowcasting GDP and Inflation: The Real Time Informational Content of Macroeconomic Data Releases," CEPR Discussion Papers 5178, C.E.P.R. Discussion Papers.
    6. Maehashi, Kohei & Shintani, Mototsugu, 2020. "Macroeconomic forecasting using factor models and machine learning: an application to Japan," Journal of the Japanese and International Economies, Elsevier, vol. 58(C).
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    More about this item

    Keywords

    Payment data; Nowcasting; ML; DFM;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E42 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Monetary Sytsems; Standards; Regimes; Government and the Monetary System

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