多哥白玉米价格的长记忆估计与分整建模
Long-Memory Estimation and Fractionally Integrated Modeling of White Maize Prices in Togo
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中文总结 AI 辅助
本研究针对多哥2001-2022年6个主要市场的白玉米价格，用多种长记忆估计量检验长程依赖，比较SARIMA等模型，发现多数市场分整模型拟合最优，凸显数据驱动选模的重要性。
中文摘要 AI 辅助
农产品价格常表现出强时间持续性，可能限制传统时间序列模型的性能。本研究调查了2001年1月至2022年6月期间多哥六个主要市场的白玉米对数月价格的长记忆性，采用Geweke-Porter-Hudak、局部惠特尔（Local Whittle）、精确局部惠特尔（Exact Local Whittle）及小波对数回归估计量进行检验。随后使用贝叶斯信息准则（Bayesian Information Criterion）与残差诊断对SARIMA、ARFIMA、SARFIMA模型进行比较，发现所有市场均存在长程依赖；分整模型对多数市场拟合最优，但部分市场仍更适合SARIMA。结果表明，长记忆证据并不必然意味着分整模型提供最优实证拟合，强调了数据驱动模型选择的重要性。
英文摘要
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.
发表机构
- Université de Kara（卡拉大学）
机构由 AI 辅助整理，请以论文原文为准。
