Identification of Classes of Bilinear Time Series Models

Usoro, Anthony E.

doi:10.18642/jsata_7100121822

Cited by 3 publications

(4 citation statements)

References 3 publications

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“…The model is made up of two parts; the linear and non-linear parts. The linear part is the aggregation of the popular autoregressive and moving average processes, while the non-linear part is the product of the two processes (Usoro, 2017). The general bilinear time series model as defined by Kendall and Ord (1990), Bibi and Oyet (1991) and Iwueze (2002) is…”

Section: Volatility Measure From Barima Modelmentioning

confidence: 99%

Volatility measure of Nigeria crude oil production as a tool to investigate production variability

Usoro¹,

Ikpang²,

George³

2020

Afr. J. Math. Comput. Sci. Res.

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The interest to carry out volatility analysis of crude oil production in Nigeria in this paper is motivated by the shortfalls in quantities of crude oil produced in recent past, given the country's high dependence on oil and its contribution to the nation's economic development. In 2016 precisely, the country experienced drastic instability in prices of crude oil at international markets and dwindling production quantities due to vandalism on oil facilities and other corrupt practices in the sector. This paper aims at using volatility measures to investigate variability of crude oil production as an assumed contributor to the economic downturn observed in the recent past in Nigeria. The data used are crude oil production data in millions of barrels collected from NNPC Statistical Bulletin. Variance of the crude oil series has been fitted with ARCH (2) model. ARCH (3) and GARCH (3,3) models are also fitted to the variance of the error obtained from ARIMA(0,1,1). ARCH and GARCH models have shown evidence of volatility in the series. The parameter estimate of the non-linear component of the bilinear model fitted to the crude oil data could not capture volatility clustering. This explains the superiority of ARCH and GARCH model over bilinear model when fitting volatile series. Synonymous with oil price volatility, evidence has it that crude oil production data are volatile. Although, Nigerian government's intervention and negotiations with Niger Delta Militants to end operational attacks on oil facilities yielded positive results, fact has been established that the two economic variables (production and price) are volatile, as contributors to the recent past economic recession in Nigeria. It is important for the stakeholders in the sector and Nigerian government to always exhibit proactive measures against illicit activities that negatively affect oil production at all times and ensure maximum control of crude oil production process and militating factors during price shocks to avoid uncontrollable economic instability.

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Section: Volatility Measure From Barima Modelmentioning

confidence: 99%

Volatility measure of Nigeria crude oil production as a tool to investigate production variability

Usoro¹,

Ikpang²,

George³

2020

Afr. J. Math. Comput. Sci. Res.

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“…Bilinear time series models are found useful in fitting revenue data of a local government area in Akwa Ibom State, [17]. Some special classes of bilinear time series models have been identified under certain conditions, [16,18]. These included Bilinear Autoregressive (BAR) models and Bilinear Moving Average (BMA) models.…”

Section: Literature Reviewmentioning

confidence: 99%

Volatility of Internally Generated Revenue and Effects of Its Major Components: A Case of Akwa Ibom State, Nigeria

Effiong¹,

Eseme²

2019

AJTAS

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In this work, volatility of Internally Generated Revenue of Akwa Ibom State with the contributory effects of its components was the major interest. Autoregressive Conditional Heteroscedasticity ARCH (1) model adopted revealed volatility in the IGR. This motivated investigation of the components as contributory factors to the volatility. The OLS regression of IGR volatility on the K-components revealed the contribution of each component to the IGR volatility. The F test result showed overall fitness of the regression model. Individual T test placed tax revenue volatility higher than any other component. The volatility in the tax revenue is explained by the inconsistency in the growing trend of the tax revenue. This is attributed to laxities in the revenue generation mechanism, therefore posing challenges to the revenue system. The revenue generation system in the state requires sound leadership in the Board of Internal Revenue, good revenue driven policy, transparent tax revenue consulting and innovative approaches by the labour force for improved revenue system. Government willingness to address the prevailing issues would enhance stability in the revenue generation, therefore, helping to reduce volatility and cope with the challenges of financial planning in Akwa Ibom State.

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“…In this section, conditions for isolation of special classes of bilinear autoregressive moving average vector models are considered. Usoro [15] identified special classes of bilinear time series models. Under certain conditions BAR and BMA were identified from the mixed BARMA model.…”

Section: Isolation Of Special Modelsmentioning

confidence: 99%

“…Under certain conditions BAR and BMA were identified from the mixed BARMA model. Here, we consider the multivariate case of Usoro [15,16]. Hence, "9" is BARV model with !…”

Section: Isolation Of Special Modelsmentioning

confidence: 99%

Necessary Conditions for Isolation of Special Classes of Bilinear Autoregressive Moving Average Vector (BARMAV) Models

Usoro¹,

Clement²

2018

AJTAS

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Bilinear Autoregressive Moving Average Vector (BARMAV) Models are models aggregated with the linear and non-linear vector components of autoregressive and moving average processes. The linear part is the sum of the two vector processes, while the non-linear part is the product of the processes. From the general BARMAV models, Bilinear Autoregressive Vector (BARV) Models and Bilinear Moving Average Vector (BMAV) Models have been isolated. Under certain conditions, the models are proved to exist. Empirically, Nigerian consumer price index and inflation rate are used to test the fitness of the bilinear models. Data for the analysis are from Central Bank of Nigeria Statistical Bulletin, collected from January 2009 to December 2016 with November 2009 as the base year for each of the series. The bilinear autoregressive moving average vector models are fitted to the data. Parameters are tested and found to be significant. The adequacy of each estimated model is confirmed with ACF, PACF and descriptive statistics adopted in the paper. The plots of the actual and fitted CPI and IR have shown that models are adequate as estimates compete favourably with the actual values. The models are useful in modelling some economic and financial data that exhibit some characteristics of non-linearity.

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Identification of Classes of Bilinear Time Series Models

Abstract: Abstractrespectively. These models are found useful in modelling most of the economic and financial data.

Cited by 3 publications

References 3 publications

Volatility measure of Nigeria crude oil production as a tool to investigate production variability

Volatility measure of Nigeria crude oil production as a tool to investigate production variability

Volatility of Internally Generated Revenue and Effects of Its Major Components: A Case of Akwa Ibom State, Nigeria

Necessary Conditions for Isolation of Special Classes of Bilinear Autoregressive Moving Average Vector (BARMAV) Models

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