The Implementation of Latent Gaussian Model in the Forecasting Process
DOI:
10.29303/jppipa.v9i12.5835Published:
2023-12-20Issue:
Vol. 9 No. 12 (2023): DecemberKeywords:
Bayesian, Forecasting Process, Latent Gaussian Model, TaxResearch Articles
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Abstract
This research aims to determine the implementation of the Latent Gaussian Model in the forecasting process. This research focuses on developing a forecasting model using the Multivariate Latent Gaussian Model (LGM) approach with shared components. which offers a more accurate representation without the assumption of stationarity and cointegration as it accommodates random components in the model. The forecasting results for the five KPPs are considered to have a very good level of accuracy with MAPE values < 10%. This shows that LGM can achieve reliable forecasting when applied to the real life problems. This condition supports forecasting and can be an effective and targeted benchmark. The Latent Gaussian Model using the Bayesian Approach in parameter estimation can be utilized in forecasting Personal Income Tax Article 25/29. This is supported by the highly accurate MAPE value of 0.01%. The implementation of the developed model is not limited to forecasting Personal Income Tax Article 25/29. but can also be used in various other fields. With its hierarchical structure. the Bayesian approach proves to be an effective method for addressing complex modeling challenges.
References
Aguerrebere. C.. Almansa. A.. Gousseau. Y.. Delon. J.. & Musé. P. (2014). A Hyperprior Bayesian Approach for Solving Image Inverse Problems. International Conference on Computational Photography. Retrieved from https://shorturl.asia/d98P2
Badan Kebijakan Fiskal. (2016). Mengelola Tantangan untuk Meningkatkan Kesejahteraan. Kementerian Keuangan. Retrieved from https://fiskal.kemenkeu.go.id/files/tekf/file/tinjauanekonomi_edisi5-2016.pdf
Bawazier, F. (2018). Reformasi Pajak di Indonesia Tax Reform In Indonesia. Jurnal Legislasi Indonesia, 8(1), 1-28. Retrieved from https://shorturl.asia/5dG4J
Blangiardo. M.. & Cameletti. M. (2015). Spatial and spatio-temporal Bayesian models with R-INLA. John Wiley and Sons. Inc.
Chiuchiolo. C. (2022). Joint Posterior Inference for Latent Gaussian Models and extended strategies using INLA. (Doctoral dissertation). Retrieved from https://repository.kaust.edu.sa/handle/10754/679224
Cyril. C. (2017). Forecasting Tax Revenue and its Volatility in Tanzania. African Journal of Economic Review. V(I). Retrieved from https://www.ajol.info/index.php/ajer/article/view/149255
Erdoğdu. H.. & Yorulmaz. R. (2019). Comparison of Tax Revenue Forecasting Models for Turkey. In S. Oktar & Y. Taşkın. 34. International Public Finance Conference (pp. 482–492). Istanbul University Press. https://doi.org/10.26650/PB/SS10.2019.001.075
Fathoni. M. I.. & Saputra. A. (2023). Forecasting Value-Added Tax (VAT) revenue using Autoregressive Integrated Moving Average (ARIMA) Box-Jenkins method. Scientax. 4(2). 205–218. https://doi.org/10.52869/st.v4i2.568
Gelman. A.. Carlin. J. B.. Stern. H. S.. Dunson. D. B.. Vehtari. A.. & Rubin. D. B. (2013). Bayesian Data Analysis Third edition. Chapman and Hall/CRC. https://doi.org/10.1201/b16018
Giacomini. R.. & Kitagawa. T. (2021). Robust Bayesian Inference for Setâ€Identified Models. Econometrica. 89(4). 1519–1556. https://doi.org/10.3982/ECTA16773
Gómez-Rubio. V.. PalmÃ-Perales. F.. López-Abente. G.. Ramis-Prieto. R.. & Fernández-Navarro. P. (2019). Bayesian joint spatio-temporal analysis of multiple diseases. SORT. Statistics and Operations Research Transactions. 43. 51–74. https://doi.org/10.2436/20.8080.02.79
Jaya. I. G. N. M.. & Andriyana. Y. (2020). Analisis Data Spasial Perspektif Bayesian. Alqaprint.
Jaya. I. G. N. M.. Chadidjah. A.. Darmawan. G.. Princidy. J. C.. & Kristiani. A. F. (2022). Does the Restriction of Human Mobility Significantly Control COVID-19 Transmission in Jakarta. Indonesia? Global Versus Local Regression Models [Preprint]. Mathematics & Computer Science. https://doi.org/10.20944/preprints202209.0271.v1
Jaya. I. G. N. M.. & Folmer. H. (2020). Bayesian spatiotemporal mapping of relative dengue disease risk in Bandung. Indonesia. Journal of Geographical Systems. 22(1). 105–142. https://doi.org/10.1007/s10109-019-00311-4
Jenkins. G. P.. Kuo. C. Y.. & Shukla. G. P. (n.d.). Tax Analysis and Revenue Forecasting: Issues and Techniques. Harvard Institute for International Development.
Kemenkeu. (2023. May 5). Reformasi Perpajakan untuk Penciptaan Keadilan. Peningkatan Kepatuhan. dan Penguatan Fiskal. Retrieved from https://fiskal.kemenkeu.go.id/publikasi/siaran-pers-detil/326
Kim. S.. & Kim. H. (2016). A new metric of absolute percentage error for intermittent demand forecasts. International Journal of Forecasting. 32(3). 669–679. https://doi.org/10.1016/j.ijforecast.2015.12.003
Lawson. A. B. (2013). Bayesian Disease Mapping Hierarchical Modeling in Spatial Epidemiology. CRC Press.
Lee. D. (2011). A comparison of conditional autoregressive models used in Bayesian disease mapping. Spatial and Spatio-Temporal Epidemiology. 2(2). 79–89. https://doi.org/10.1016/j.sste.2011.03.001
Martino. S.. & Riebler. A. (2020). Integrated Nested Laplace Approximations (INLA). Wiley StatsRef: Statistics Reference Online. 1–19. https://doi.org/10.1002/9781118445112.stat08212
Morrison. K. T.. Shaddick. G.. Henderson. S. B.. & Buckeridge. D. L. (2016). A latent process model for forecasting multiple time series in environmental public health surveillance: A latent process model for forecasting multiple time series in environmental public health surveillance. Statistics in Medicine. 35(18). 3085–3100. https://doi.org/10.1002/sim.6904
Ofori. M. S.. Fumey. A.. & Nketiah-Amponsah. E. (2020). Forecasting Value Added Tax Revenue in Ghana. 4(2). Retrieved from https://ojs.tripaledu.com/jefa/article/view/58
Oleson. J. J.. Smith. B. J.. & Kim. H. (2021). Joint Spatio-Temporal Modeling of Low Incidence Cancers Sharing Common Risk Factors. Journal of Data Science. 6(1). 105–123. https://doi.org/10.6339/JDS.2008.06(1).382
Opitz, T. (2017). Latent Gaussian modeling and INLA: A review with focus on space-time applications. Journal de la société française de statistique, 158(3), 62-85. Retrieved from http://www.numdam.org/item/JSFS_2017__158_3_62_0/
Richardson, S., Abellan, J. J., & Best, N. (2006). Bayesian spatio-temporal analysis of joint patterns of male and female lung cancer risks in Yorkshire (UK). Statistical methods in medical research, 15(4), 385-407. https://doi.org/10.1191/0962280206sm458oa
Robert. C.. & Casella. G. (2011). A Short History of Markov Chain Monte Carlo: Subjective Recollections from Incomplete Data. Statistical Science. 26(1). https://doi.org/10.1214/10-STS351
Rue. H.. & Held. L. (2005). Gaussian Markov Random Fields. Chapman and Hall/CRC.
Rue. H.. Martino. S.. & Chopin. N. (2009). Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Laplace Approximations. Journal of the Royal Statistical Society Series B: Statistical Methodology. 71(2). 319–392. https://doi.org/10.1111/j.1467-9868.2008.00700.x
Van De Schoot. R.. Depaoli. S.. King. R.. Kramer. B.. Märtens. K.. Tadesse. M. G.. Vannucci. M.. Gelman. A.. Veen. D.. Willemsen. J.. & Yau. C. (2021). Bayesian statistics and modelling. Nature Reviews Methods Primers. 1(1). 1. https://doi.org/10.1038/s43586-020-00001-2
Van De Schoot. R.. Kaplan. D.. Denissen. J.. Asendorpf. J. B.. Neyer. F. J.. & Van Aken. M. A. G. (2014). A Gentle Introduction to Bayesian Analysis: Applications to Developmental Research. Child Development. 85(3). 842–860. https://doi.org/10.1111/cdev.12169
Wagenmakers. E.-J.. Lee. M.. Lodewyckx. T.. & Iverson. G. J. (2008). Bayesian Versus Frequentist Inference. In H. Hoijtink. I. Klugkist. & P. A. Boelen (Eds.). Bayesian Evaluation of Informative Hypotheses (pp. 181–207). Springer New York. https://doi.org/10.1007/978-0-387-09612-4_9
Wasserman, L. A. (1989). A robust Bayesian interpretation of likelihood regions. The Annals of Statistics, 17(3), 1387-1393. Retrieved from https://projecteuclid.org/journals/annals-of-statistics/volume-17/issue-3/A-Robust-Bayesian-Interpretation-of-Likelihood-Regions/10.1214/aos/1176347277.short
Wei. W. W. S. (2019). Multivariate Time Series Analysis and Applications. John Wiley & Sons Ltd.
Author Biographies
Elyn Prina, Department of Statistics, Universitas Padjadjaran
Yusep Suparman, Post-Graduate Program in Applied Statistics, Faculty of Mathematics and Natural Sciences, Padjadjaran University, Sumedang 45363, Indonesia
Gumgum Darmawan, Department of Statistics, Faculty of Mathematics and Natural Sciences, Padjadjaran University, Sumedang 45363, Indonesia
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