Scholarly works

Permanent URI for this collectionhttps://repository.ui.edu.ng/handle/123456789/422

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Subject: search.filters.subject.Bayesian inference

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    A time varying parameter state-space model for analyzing money supply-economic growth nexus
    (2015-03) Awe, O.O.; Crandell, I.; Adepoju, A.A.; Leman, S.
    In this paper, we propose a time-varying parameter state space model for analyzing predictive nexus of key economic indicators such as money supply and Gross Domestic Product (GDP). Economic indicators are mainly used for measuring economic trends. Policy makers in both advanced and developing nations make use of economic indicators like GDP to predict the direction of aggregate economic activities. We apply the Kalman filter and Markov chain Monte Carlo algorithm to perform posterior Bayesian inference on state parameters specified from a discount Dynamic Linear Model (DLM), which implicitly describes the relationship between response of GDP and other economic indicators of an economy. In our initial exploratory analysis, we investigate the predictive ability of money supply with respect to economic growth, using the economy of Nigeria as a case study with an additional evidence from South African economy. Further investigations reveal that leading variables like capital expenditure, the exchange rate, and the treasury bill rate are also useful for forecasting the GDP of an economy. We demonstrate that by using these various regressors, there is a substantial improvement in economic forecasting when compared to univariate random walk models
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    Bayesian optimal filtering in dynamic linear models: an empirical study of economic time series data
    (2015) Awe, O. O.; Adepoju, A. A.
    This paper reviews a recursive Bayesian methodology for optimal data cleaning and filtering of economic time series data with the aim of using the Kalman filter to estimate the parameters of a specified state space model which describes an economic phenomena under study. The Kalman filter, being a recursive algorithm, is ideal for usage on time-dependent data. As an example, the yearly measurements of eight key economic time series data of the Nigerian economy is used to demonstrate that the integrated random walk model is suitable for modeling time series with no clear trend or seasonal variation. We find that the Kalman filter is both predictive and adaptive, as it looks forward with an estimate of the variance and mean of the time series one step into the future and it does not require stationarity of the time series data considered