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PreprintCC BY 4.0arXiv

Long-Memory Estimation and Fractionally Integrated Modeling of White Maize Prices in Togo

  • Manganaw N'Daam
  • Edoh Katchekpele
  • Tchilabalo Abozou Kpanzou
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Abstract

Agricultural commodity prices often exhibit strong temporal persistence, which may limit the performance of conventional time series models. This study investigates long memory in logarithmic monthly white maize prices from six major markets in Togo between January 2001 and June 2022. Long memory is examined using the Geweke--Porter--Hudak, Local Whittle, Exact Local Whittle, and wavelet log-regression estimators. SARIMA, ARFIMA, and SARFIMA models are subsequently compared using the Bayesian Information Criterion and residual diagnostics. Long-range dependence is found across all markets. Fractionally integrated models provide the best fit for most markets, although SARIMA remains preferable for some. The results demonstrate that evidence of long memory does not necessarily imply that a fractionally integrated model provides the best empirical fit, emphasizing the importance of data-driven model selection.

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Cite

BibTeXRIS
BibTeX
@misc{ndaam2026longmemory,
  title = {{Long-Memory Estimation and Fractionally Integrated Modeling of White Maize Prices in Togo}},
  author = {Manganaw N'Daam and Edoh Katchekpele and Tchilabalo Abozou Kpanzou},
  howpublished = {arXiv},
  year = {2026},
  month = aug,
  doi = {10.48550/arxiv.2608.30569},
  eprint = {2608.30569},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2608.30569},
  note = {CC BY 4.0},
}
RIS
TY  - UNPB
TI  - Long-Memory Estimation and Fractionally Integrated Modeling of White Maize Prices in Togo
AU  - Manganaw N'Daam
AU  - Edoh Katchekpele
AU  - Tchilabalo Abozou Kpanzou
PY  - 2026
DA  - 2026/08/31
PB  - arXiv
DO  - 10.48550/arxiv.2608.30569
UR  - https://arxiv.org/abs/2608.30569
AB  - Agricultural commodity prices often exhibit strong temporal persistence, which may limit the performance of conventional time series models. This study investigates long memory in logarithmic monthly white maize prices from six major markets in Togo between January 2001 and June 2022. Long memory is examined using the Geweke--Porter--Hudak, Local Whittle, Exact Local Whittle, and wavelet log-regression estimators. SARIMA, ARFIMA, and SARFIMA models are subsequently compared using the Bayesian Information Criterion and residual diagnostics. Long-range dependence is found across all markets. Fractionally integrated models provide the best fit for most markets, although SARIMA remains preferable for some. The results demonstrate that evidence of long memory does not necessarily imply that a fractionally integrated model provides the best empirical fit, emphasizing the importance of data-driven model selection.
ER  -