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FORECASTING INFLATION RATES IN GHANA USING REGRESSION, ARTIFICIAL NEURAL NETWORKS AND SUPPORT VECTOR MACHINES

  • JoTEMaCS
  • January 12, 2021
  • 4:43 am
  • No Comments

Comprehending the quintessence of the affiliation of macro-economic variables is very decisive to efficient and successful economic decision-making (Asamoah, 2013; Hoti, McAleer & Pauwels, 2008; Wen et al., 2010) [10, 33, 73]. However, the highly volatile nature of macro-economic variables makes accurate predictions of macro-economic data very challenging for analysis (Asamoah, 2013; Kampouridis, Alsheddy, & Tsang, 2013). [10, 36].

A very substantial macro-economic variable that is often pursued by many countries’ central banks for forecasting purposes is inflation. Inflation is illustrated as an unrelenting rise in the general price of goods and services in the economy over time (Barro & Grilli, 1994). [12].  As the universal price level increases, each currency unit consumes fewer goods and services; as a result, inflation exemplifies a diminution of buying power per unit of capital – a loss in economic interest in the medium and unit of account of the economy (Walgenbach, Dittrich & Hanson, 1973). [72]………………………………………

Issue Keywords:

  • Artificial Neural Networks, Forecasting, Inflation Rates, Regression, Support Vector Machines

Issue Category:

  • Finance, Ghana, INFLATION
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PreviousEFFECTS OF EMOTIONAL INTELLIGENCE VARIABLES ON INVESTOR BEHAVIOUR IN GHANA

FORECASTING INFLATION RATES IN GHANA USING REGRESSION, ARTIFICIAL NEURAL NETWORKS AND SUPPORT VECTOR MACHINES

Comprehending the quintessence of the affiliation of macro-economic variables is...

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JoTEMaCS
January 12, 2021
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