2016Unpublished venueRequires access

Modeling and Forecasting of Foreign Direct Investment (FDI) Inflows to Ghana (1994-2010)

Samuel Lartey, Enock Mintah Ampaw, Kwame Asare Gyasi-Agyei, Mark Nte-Adik Nborlen

Open publisher page 1 citations

Abstract

This paper attempts to examine quarterly univariate data of Foreign Direct Investment (FDI) inflows to Ghana spanning from the year 1994 to2010, using the Auto regression Moving Averages (ARMA) model. It extends its scope by comparing the trend and the ARMA model to determine which of them predicts estimates that are close to their actuals. Statistical tools such as trend estimation and the Box - Jenkins methodology for building ARIMA models were employed. Results from the study indicate that, FDI grew on the average by 11.1% for the period understudy. However, the ARMA (1, 1) model with a drift is the best fit and predicted estimates close to its actual than the trend model, even though very marginal for the period.

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What this paper is about

This paper attempts to examine quarterly univariate data of Foreign Direct Investment (FDI) inflows to Ghana spanning from the year 1994 to2010, using the Auto regression Moving Averages (ARMA) model. It extends its scope by comparing the trend and the ARMA model to determine which of them predicts estimates that are close to their actuals. Statistical tools such as trend estimation and the Box - Jenkins methodology for building ARIMA models were employed. Results from the study indicate that, FDI grew on the average by 11.1% for the period understudy. However, the ARMA (1, 1) model with a drift is the best fit and predicted estimates close to its actual than the trend model, even though very marginal for the period.

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Available abstract

This paper attempts to examine quarterly univariate data of Foreign Direct Investment (FDI) inflows to Ghana spanning from the year 1994 to2010, using the Auto regression Moving Averages (ARMA) model. It extends its scope by comparing the trend and the ARMA model to determine which of them predicts estimates that are close to their actuals. Statistical tools such as trend estimation and the Box - Jenkins methodology for building ARIMA models were employed. Results from the study indicate that, FDI grew on the average by 11.1% for the period understudy. However, the ARMA (1, 1) model with a drift is the best fit and predicted estimates close to its actual than the trend model, even though very marginal for the period.

Key concepts: Foreign direct investment, Autoregressive integrated moving average, Box–Jenkins, Econometrics, Univariate, Moving average, Estimation, Economics

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