BOYAYO Theodette

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Théodette Boyayo received her law degree from the Université Lumière de Bujumbura,Burundi. She subsequently obtained her Master’s in International European Law at the University of Nantes, France.She is currently pursuing her PhD on the topic of the creation of a unique VAT area in the countries of East Africa and the lessons learned from the European Union at the University of Burundi.She is olso a staste attorney in tax and customs matter at the Office Burundais des Recettes.

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Daniel D. Hutto

University of Wollongong

Galen Strawson

The University of Texas at Austin

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University of Cologne

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University of Memphis

George Lawson

The Australian National University

Gary Feinman

Field Museum

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Johns Hopkins University

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LUISS Guido Carli

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University of Navarra

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European University at St. Petersburg

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Papers by BOYAYO Theodette

Statistical Model for Analyzing and Predicting Burundian Tax Revenues: Case Study of Burundi Revenue Authority

Zenodo (CERN European Organization for Nuclear Research), Mar 18, 2023

To model and forecast monthly tax revenue collection, and propose policy recommendations for stab... more To model and forecast monthly tax revenue collection, and propose policy recommendations for stabilization of revenue collection volatility are our objectives in this study. Data analysis of the evolution of tax revenues shows a slight growth and the Box.com test gives |λ|=0.07 which leads to a check logarithmic transformation. The study revealed a positive trend in data with an amplitude of variations increasing slightly over time. It showed also a quarterly seasonality, highlighting a peak in each third month of each year and a relative increase in the first two quarters. The correlogram and partial correlogram show respectively the lag of orders 3 and 12 and 3 and 9 which was significant and lead to conclude the seasonal autoregressive coefficient. Then, the Dickey-Fuller test (p-value < 0.01) and Phillips Perron test (p-value < 0.01) come up to confirm the stationarity of the series under study. ARIMA (0,0,0) (2,1,2) [4] and ARIMA (0,0,0) (2,1,1) [4] models have an AIC of 359.44 and 359.43 respectively, which is smaller than the other models and have been selected for analyzing and predicting the tax revenues of Burundi. But specifically, the study used ARIMA (0,0,0) (2,1,2) [4] which is better than ARIMA (0,0,0) (2,1,1) [4] because of its least parameters. The study thought that this model is recommendable for this institution, which supports the governmental constraints.