Long memory, seasonality and time trends in the average monthly rainfall in major cities of Nigeria

Artikel

Several features may be present in rainfall data, and sophisticated time series procedures are needed for the analysis. These features are that of seasonality, long range dependency of observations and time trend as observed in the climatological series. This paper therefore considered the analysis of these features in the monthly rainfall data of 37 meteorological stations across the six geo-political zones of Nigeria between 1981 and 2013. A fractional integration technique of time series analysis which permits the feature of rainfall time series to be examined in a single framework is employed. The procedure gives explicit formulas and directions that allow a moderately sophisticated analyst to perform trend analysis. The results show that the trend and persistence of long memory are fairly distributed across the six geopolitical zones such that a zone cannot be singled out with intense or abnormal rainfall distribution. By removing the seasonal variation and focussing on the rainfall anomalies with respect to the average monthly means, we found significant time trend coefficients that are positive, thus indicating that monthly rainfall has increased during the sampled periods.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: CBN Journal of Applied Statistics ; ISSN: 2476-8472 ; Volume: 06 ; Year: 2015 ; Issue: 2 ; Pages: 39-58 ; Abuja: The Central Bank of Nigeria

Classification and Topics

• Classification: Wirtschaft ; Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
• Subject: Long range dependence ; Rainfall ; Seasonality ; Trend coefficient

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Yaya, OlaOluwa S. ; Gil-Alaña, Luis A. ; Akomolafe, Abayomi A.
• Event: Veröffentlichung
• (who): The Central Bank of Nigeria
• (where): Abuja
• (when): 2015

Further information

• Handle: http://hdl.handle.net/10419/142105
• Last update: 10.03.2025, 11:42 AM CET