The country ICT level and the Fintech firm Performance: Evidence from BRICS ‎Countries ‎

NEIFAR, MALIKA and Gharbi, Leila (2025): The country ICT level and the Fintech firm Performance: Evidence from BRICS ‎Countries ‎.

Abstract

Purpose: The scope of this paper is to investigate if the information and communications ‎technology (ICT) can improve the FinTech firm performance in the BRICS countries from ‎monthly macro time series data during 2014M01-2022M12. ‎ Design/methodology/approach: Through the Bayesian VAR-X approach and the time series ‎DYNARDL simulation models, we investigate the impact of the ICT and its components on ‎the firm performance for both the short-run (SR) and the long-run (LR) historical and ‎predictive trend. Besides these regression models, this study applies the Granger Causality ‎‎(GC) in quantile and the frequency domain (FD) GC tests to show more details about the ‎causality linkage.‎ Findings: From the BVAR-X approach, historical IRFs conclude that the ICT has positive ‎effect on PI for all countries in the SR and a positive effect in the LR only for China. From ‎the DYNARDL simulation models, predictive IRFs results corroborate with the historical ‎IRFs results except for the China and SA in the SR and for Brazil and India in the LR. We ‎conclude in addition that the predictive positive relationships is driven by MCS for Brazil, IUI ‎for China, FBS for SA, and all of the ICT components for the India case. GC type test results ‎are in accordance with previous results. ‎ Originality: The novelty of this research is based on the idea of studying the effect of the ICT ‎on FinTech firm performance by using several time series data based dynamic technics so that ‎we can estimate and predict the SR adjustments that arise from the impact of ICT to the LR ‎relationship with the firm profitability. ‎

• Item Type: MPRA Paper
• Language: English
• Keywords: FinTech Firm from BRICS area; Bayesian VAR-X model; DYNARDL simulation model; ‎Historical and predictive IRFs for SR and LR effects; Granger Causality test in quantile ‎‎(QGC); Frequency domain Granger causality (FDC) test
• Subjects: C - Mathematical and Quantitative Methods > C0 - General > C01 - Econometrics; C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C11 - Bayesian Analysis: General; C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; D - Microeconomics > D2 - Production and Organizations > D22 - Firm Behavior: Empirical Analysis

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