Application of SARIMA to Modelling and Forecasting Money Circulation in Nigeria

Adubisi, Obinna Damain; Eleke, Chinemerem; Mom, T; Adubisi, C.E.

doi:10.9734/arjom/2017/35555

Cited by 4 publications

(4 citation statements)

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“…The trend and pattern of money circulation in Nigeria are reviewed, and Box-Jenkins methodology was used to analyze the monthly records of money in circulation that were received from the central bank of Nigeria. According to Adubisi et al (2017), the Seasonal ARIMA (2, 1, 0) (0, 1, 1)12 model is suitable for describing the patterns shown in the dataset. On Turkish monetary aggregates, the performances of two seasonal adjustment methods X-12 ARIMA and TRAMO/SEATS as well as several important factors that must be taken into account throughout the seasonal adjustment process were examined.…”

Section: Empirical Review Of Literaturementioning

confidence: 99%

Predictive Modeling of Nigeria’s Currency in Circulation Using X-12 Autoregressive Integrated Moving Average Method

Bamanga

Adams

2023

DUTCH J FINANCE MANA

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In Nigeria, the average monthly quantity of currency in circulation (CIC) has increased by 269 billion nairas, reaching 2.13 trillion as of 2019 and 2.41 trillion as of 2020. The current value of currency in circulation is expected to be 2.88 trillion naira. The economy of Nigeria is impacted by the seasonal fluctuations in its currency, and it is unavoidable that the economy would need to be adjusted. The purpose of this study was to adjust the seasonal effect of eight days to Easter and Muslim holidays on CIC, model and predict the CIC in Nigeria using the United State Census Bureau's X-12 ARIMA Seasonal adjustment software. The data utilized in the study was the monthly amount of money in circulation that was taken from the Central Bank of Nigeria (CBN) Bulletin between January 2012 and March 2022. Natural logarithm was used to standardize the data, and series seasonality was removed using seasonal differencing. Based on these data, it is clear that X-12-ARIMA (2 1 1)(0 1 1) is the most accurate forecasting approach for Nigeria's CIC. The money in circulation in Nigeria from April 2022 through December 2022 will rise at a positive rate of 2.8% growth rate each month, with a predicted monthly mean CIC of 3.40 trillion by the end of the year 2022, according to this method's predictions. This is the first study on modeling and forecast of CIC in Nigeria that have utilize the United State Census Bureau X-12-ARIMA software, the findings can be extrapolated to the coming year, Nigerians may want to get ready for an increase in the amount of money in circulation during this time.

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Section: Empirical Review Of Literaturementioning

confidence: 99%

Predictive Modeling of Nigeria’s Currency in Circulation Using X-12 Autoregressive Integrated Moving Average Method

Bamanga

Adams

2023

DUTCH J FINANCE MANA

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“…A number of studies using the Box-Jenkins approach have been carried out (Shakira, 2011) with exploring the application of the Box-Jenkins approach to stock prices modelling at different sampling time intervals in order to determine if there is an optimal frame and similarities in autocorrelation patterns of stocks within the same industry. Adubisi et al (2017a) developed a seasonal ARIMA model with Square-Root variance stabilizing transformation for monitoring Nigeria crude oil export to America. The result of the study showed that the Square-Root transformed series performed better in capturing the dynamics of the system as against the normal variance stabilization using the logarithm transformation.…”

Section: Introductionmentioning

confidence: 99%

“…They also suggested in the paper that an ARIMA intervention time series analysis could be used to forecast the peak value of production and utilization data. Also, Adubisi et al (2017b) explored the trend and pattern of Nigeria money in circulation system within a specified period. The study revealed a steady rise in Nigeria money in circulation in the three years forecast periods produced by the seasonal ARIMA model.…”

Section: Introductionmentioning

confidence: 99%

A predictive Autoregressive Integrated Moving Average (ARIMA) Model for forecasting inflation rates

Awa¹,

David²,

Adubisi³

et al. 2018

Res. J. Bus. Econ. Manag.

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This paper considers the modelling and forecasting of monthly All-items (12 months average change) inflation rates in Nigeria using the Box-Jenkins (ARIMA) model. Time series data used in the study was collected from the Central Bank of Nigeria statistical web database. The data was differenced twice to achieve stationarity in the series as required. Based on the evaluation and diagnostic criteria, the most accurate model is selected. The order of the best ARIMA model was found to be ARIMA (1, 2, 1). The diagnostic analysis of the model residuals showed that they are normally distributed uncorrelated random shocks. The findings in this study showed that the selected ARIMA model captured the dynamics in the series and produced forecasted values which had minimal forecast errors when compared with the actual inflation values in the validation period.

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“…Adubisi and Jolayemi [7] used ARIMA-intervention analysis modelling approach to evaluate and estimate the impact of the financial crisis on Nigeria Crude oil export. Furthermore, Smart [8] explored the feasibility for application of Box-Jenkins Approach (ARIMA) in modelling and forecasting maternal mortality Ratios (MMR) and Adubisi et al; [9] used the seasonal ARIMA to model the Nigeria money in circulation series and also produced a three years forecast values using the fitted model.…”

Section: Introductionmentioning

confidence: 99%

Predictive Model with Square-Root Variance Stabilizing Transformation for Nigeria Crude Oil Export to America

Adubisi¹

2017

SJAMS

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Abstract:In the last few decades, crude oil has claimed the topmost position in Nigerian export list, constituting a very fundamental change in the structure of Nigerian international trade. In this study, secondary data on monthly crude oil export to the United States was obtained from the Energy Information Administration (EIA) database. Using the Box-Jenkins (ARIMA) methodology, the results showed that Seasonal ARIMA (0, 1, 1) (1, 0, 1) 12 model had the least information criteria after the data was Square-Root transformed and non-seasonally first differenced in order to achieve series stationarity. The diagnostic tests on the selected model residuals revealed the residuals are normally distributed uncorrelated random shocks.

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Application of SARIMA to Modelling and Forecasting Money Circulation in Nigeria

Cited by 4 publications

References 0 publications

Predictive Modeling of Nigeria’s Currency in Circulation Using X-12 Autoregressive Integrated Moving Average Method

Predictive Modeling of Nigeria’s Currency in Circulation Using X-12 Autoregressive Integrated Moving Average Method

A predictive Autoregressive Integrated Moving Average (ARIMA) Model for forecasting inflation rates

Predictive Model with Square-Root Variance Stabilizing Transformation for Nigeria Crude Oil Export to America

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