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Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application
Articles

COMPARATIVE ANALYSIS OF TWO-STEP AND QUASI MAXIMUM LIKELIHOOD ESTIMATION IN THE DYNAMIC FACTOR MODEL FOR NOWCASTING GDP GROWTH IN INDONESIA

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  • Gilbert Alvaro Souisa,
  • Reyner M. Leiwakabessy,
  • Salma Damayanti,
  • Mohammad Zanuar F Terim,
  • Shelma M Pelu
Gilbert Alvaro Souisa
Departement Of Statistics, Faculty of Sciences and Data Analytics, Institut Teknologi Sepuluh Nopember, Indonesia
Reyner M. Leiwakabessy
Departement Of Statistics, Faculty of Sciences and Data Analytics, Institut Teknologi Sepuluh Nopember, Indonesia
Salma Damayanti
Departement Of Statistics, Faculty of Sciences and Data Analytics, Institut Teknologi Sepuluh Nopember, Indonesia
Mohammad Zanuar F Terim
Departement Of Statistics, Faculty of Sciences and Data Analytics, Institut Teknologi Sepuluh Nopember, Indonesia
Shelma M Pelu
Actuarial Study Program, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Indonesia
DOI: https://doi.org/10.30598/barekengvol19iss1pp655-664
Published January 13, 2025
Keywords
  • Nowcasting,
  • Dynamic Factor Model,
  • Gross Domestic Product
How to Cite
[1]
G. A. Souisa, R. M. Leiwakabessy, S. Damayanti, M. Z. F. Terim, and S. M. Pelu, “COMPARATIVE ANALYSIS OF TWO-STEP AND QUASI MAXIMUM LIKELIHOOD ESTIMATION IN THE DYNAMIC FACTOR MODEL FOR NOWCASTING GDP GROWTH IN INDONESIA”, BAREKENG: J. Math. & App., vol. 19, no. 1, pp. 655-664, Jan. 2025.

Copyright (c) 2025 Gilbert Alvaro Souisa, Reyner M. Leiwakabessy, Salma Damayanti, Mohammad Zanuar F Terim, Shelma M Pelu

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Abstract

Economic activity data is needed quickly to make policy decisions, but this data suffers from publication delays. Gross Domestic Product (GDP) data is released within five weeks after the end of the quarter. An effort that can be made to provide such data is through nowcasting, which is forecasting in the current period using variables that have a higher frequency. This study aims at nowcasting GDP growth. The nowcasting method used is the Dynamic Factor Model (DFM) with Two Step (TS) and Quasi Maximum Likelihood (QML) estimation. The nowcasting results show that the DFM-TS model is better than the DFM-QML because it has a larger adjusted R-squared value and has the smallest RMSE value of 1.71035 compared to the DFM-QML value, which has an RMSE value of 1.71598.

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