A Time Varying Parameter State-Space Model for Analyzing Money Supply-Economic Growth Nexus
Olushina Awe (),
A. Adedayo Adepoju and
Journal of Statistical and Econometric Methods, 2015, vol. 4, issue 1, 4
In this paper, we propose a time-varying parameter state spaceÂ model for analyzing predictive nexus of key economic indicators suchÂ as money supply and Gross Domestic Product (GDP). Economic indicatorsÂ are mainly used for measuring economic trends. Policy makers inÂ both advanced and developing nations make use of economic indicatorsÂ like GDP to predict the direction of aggregate economic activities.Â We apply the Kalman filter and Markov chain Monte Carlo algorithm toÂ perform posterior Bayesian inference on state parameters specified fromÂ a discount Dynamic Linear Model (DLM), which implicitly describesÂ the relationship between response of GDP and other economic indicatorsÂ of an economy. In our initial exploratory analysis, we investigateÂ the predictive ability of money supply with respect to economic growth,Â using the economy of Nigeria as a case study with an additional evidenceÂ from South African economy. Further investigations reveal that leading variables like capital expenditure, the exchange rate, and theÂ treasury bill rate are also useful for forecasting the GDP of an economy.Â We demonstrate that by using these various regressors, there isÂ a substantial improvement in economic forecasting when compared toÂ univariate random walk models.
References: Add references at CitEc
Citations: View citations in EconPapers (2) Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:spt:stecon:v:4:y:2015:i:1:f:4_1_4
Access Statistics for this article
More articles in Journal of Statistical and Econometric Methods from SCIENPRESS Ltd
Bibliographic data for series maintained by Eleftherios Spyromitros-Xioufis ().